Optimizing Energy Performance of Phase-Change Material-Enhanced Building Envelopes Through Novel Performance Indicators

Abstract

Over recent decades, phase-change materials (PCMs) have gained prominence as latent-heat thermal energy storage systems in building envelopes because of their high energy density. However, only PCMs that complete a full daily charge–discharge cycle can deliver meaningful energy and carbon-emission savings. This simulation study introduces a methodology that simultaneously optimizes PCM integration for storage efficiency, indoor thermal comfort, and energy savings. Two new indicators are proposed: overall storage efficiency (EC_n), which consolidates heating and cooling-efficiency ratios into a single value, and the performance factor (PF), which quantifies the PCM’s effectiveness in maintaining thermal comfort. Using EnergyPlus v8.9 coupled with DesignBuilder, a residential ASHRAE 90.1 mid-rise apartment was modeled in six warm-temperate (Cfb) European cities for the summer period from June 1 to August 31. Four paraffin PCMs (RT-22/25/28/31 HC, 20 mm thickness) were tested under natural and controlled ventilation strategies, with windows opening 50% when outdoor air was at least 2 °C cooler than indoors. Simulation outputs were validated against experimental cubicle data, yielding a mean absolute indoor temperature error ≤ 4.5%, well within the ±5% tolerance commonly accepted for building thermal simulations. The optimum configuration—RT-25 HC with temperature-controlled ventilation—achieved PF = 1.0 (100% comfort compliance) in all six cities and delivered summer cooling-energy savings of up to 3376 kWh in Paris, the highest among the locations studied. Carbon-emission reductions reached 2254 kg CO₂-e year⁻¹, and static payback periods remained below the assumed 50-year building life at a per kg PCM cost of USD 1. The EC_n–PF framework, therefore, provides a transparent basis for selecting cost-effective, energy-efficient, and low-carbon PCM solutions in warm-temperate buildings.

1. Introduction

The building sector is recognized as a significant contributor to global energy use. As a major world energy consumer, buildings are crucial in shaping the worldwide energy and environmental landscape since they contribute to over 36% of total energy consumption, 33% of greenhouse gas emissions, and 40% of material usage [1]. Today, around 5 billion people need significant space cooling, and this number is projected to reach 7 billion by 2050, adding another 2.9 billion to the existing 1.5 billion air conditioners due to population growth and climate change [2]. The huge energy consumption by heating, ventilating, and air conditioning (HVAC) systems in buildings, if reduced, can be beneficial in reducing the overall energy consumption by the building sector and associated carbon emissions.

In this regard, passive design strategies are being developed to address the ever-increasing issue of electricity consumption for building space conditioning. Passive designs represent approaches that use local climate and building features to optimize indoor conditions and reduce energy consumption. One such promising passive strategy is integrating phase-change materials (PCMs) into the building envelope, which maintains thermal comfort, increases energy efficiency, and yields economic and environmental benefits [3,4]. PCMs can store a significant quantity of thermal energy as latent heat, which can be absorbed or released as the material’s phase transforms from solid to liquid and vice versa [5,6]. The previously enumerated benefits, compatibility with any surrounding environment, and the simplicity of PCM incorporation make it an excellent option for use in buildings.

Since it is difficult to construct actual PCM-integrated buildings to evaluate their performance, numerical simulation has become a vital tool for assessing the efficacy of PCM-integrated buildings [7,8]. Several numerical studies have been conducted across the globe to determine the thermal and energy efficiency of PCM-integrated buildings [9,10,11]. Terhan and Ilgar [9] analyzed the energy performance of a building’s exterior walls in terms of heating and cooling. This analysis involved integrating two different PCMs with varying melting temperatures and wall thicknesses. The results of this analysis revealed that the utilization of the optimal thickness and PCM led to energy savings of 14.76% for heating and 24.45% for cooling. Similarly, both the energy savings and thermal performance of PCM-integrated walls in a building were studied by Anter et al. [10]. Various PCM wall thicknesses, types, and locations were analyzed for the climate of Aswan, Egypt. PCM with a melting point of 35 °C (RT-35HC) yielded the highest thermal performance with an average reduction of 3.4 °C in the indoor wall surface temperature. In the summer, the same PCM, with an optimal location of 1.5 cm from the inside and outside walls, results in a 66% decrease in energy gains. In another study [11], numerical simulations were carried out on PCM-incorporated buildings for the semi-arid climate zone. The results demonstrated that PCM enhances cooling and heating energy efficiency, thermal comfort, and temperature fluctuations. The optimized PCM configuration results in an annual average temperature fluctuation reduction of 1.91 °C and energy savings of 102 kWh for heating and 324 kWh for cooling.

PCM-integrated buildings offer considerable energy savings; however, for optimal performance, PCM must complete the melting and solidification cycle within 24 h. In the daytime, it should melt and absorb the heat; at night, it should emit the absorbed heat to solidify again to be an efficient passive system. For thermal regulation of the indoor environment, the PCM layer is typically installed in the building’s inner surface, making it difficult for heat to escape to the exterior and increasing the cooling energy demand [12]. Therefore, night ventilation is considered a means of recharging the PCM overnight in the buildings [13,14,15,16]. Khawaja and Memon [13] simulated a mid-rise residential building for future climatic scenarios (2095) in 13 climate zones across the globe using the Koppen–Geiger climatic classification. They found that PCMs combined with changeover ventilation regulated by the temperature differential between the interior and exterior led to energy savings of up to 96%. In another study [14], the influence of night ventilation duration on the thermal performance of a PCM-integrated room under hot summer conditions was examined for six days in a row. It was found that when the night ventilation duration was increased from one to four hours, it reduced the indoor average air temperature of the PCM-integrated room by 28.6%. Different ventilation strategies were coupled with a PCM-incorporated office building in a study by Prabhakar et al. [15]. The efficiency of PCM improved from 3.3 to 25.6% with night ventilation in the temperate climatic zone, and it leaped to 40% when combined with temperature-regulated ventilation.

In addition to energy savings and thermal comfort, some researchers have developed novel indices to quantify the performance of PCM in the building envelope. Evola et al. [17] present two new indicators to understand the behavior of microencapsulated PCM wallboard. The microencapsulated PCM wallboard contained 60% microencapsulated PCM and had a peak melting temperature of 27.6 °C. They formulate the Frequency of Activation (FA) as the share of a 24 h cycle in which the PCM surface temperature remains within its melting range (22–28.5 °C), thus indicating the likelihood that latent storage can occur. Recognizing that FA disregards the strong temperature dependence of the equivalent heat capacity, they introduce the PCM storage efficiency (η_PCM)—the ratio of the daily energy actually accumulated, obtained by integrating the positive heat fluxes entering the wallboard from both faces, to the panel’s latent capacity L = 132 Wh m⁻². Together, FA measures the occurrence of activation, whereas η_PCM quantifies the extent to which latent heat is effectively exploited, yielding a more comprehensive evaluation of PCM performance in building envelopes. Evola et al.’s [17] indicators are based on a wallboard that contains only 60 wt% micro-encapsulated PCM. The remaining 40 wt% of non-PCM material still absorbs and releases sensible heat, and this heat is included in the measured flux; consequently, the calculated indicator overstates the true latent-energy contribution of the PCM. Moreover, in a typical building construction that features multilayers, this approach cannot be used to isolate the energy stored in the PCM. In another study [18], cooling and heating energy coefficients (CE and HE) were proposed to determine a PCM’s charge–discharge capacity over a 24-h cycle. The diurnal charge and discharge fractions—defined as the energy stored or released within one day, divided by the PCM’s latent capacity—were first calculated. Equal weights were then assigned to the charging and discharging periods (12 h of charging and 12 h of discharging), on the premise that latent-heat performance depends not only on the total energy exchanged but also on its timing: a PCM that reaches full capacity early and remains inactive for the remainder of the cycle is less effective than one that stays partially molten and continues to absorb and release heat throughout the full 24-h period. In addition, an indicator for PCM’s effectiveness (e) was developed, which is defined as the percentage of time the interior operative temperature remains within the ASHRAE 80% acceptable status. This paper [18] presents cooling and heating efficiency coefficients based on a limited dataset spanning only two summer days, which is insufficient to account for the variability inherent in diurnal conditions, climatic fluctuations, and dynamic thermal loads. Additionally, the assumption of idealized 12-hour charging and 12-hour discharging periods does not reflect the actual thermal behavior of PCMs in building envelopes, where the durations of charge and discharge are governed by external conditions and system interactions. The independent reporting of cooling and heating coefficients further complicates performance assessment, as a PCM may exhibit high efficiency in one phase but low in the other, offering no clear basis for comparative evaluation or design optimization. These methodological constraints highlight the need for alternative indicators that (i) are evaluated over longer, climatically representative periods; (ii) account for actual charge–discharge timings; and (iii) consolidate cooling and heating performance into a single, representative indicator—objectives which the present study seeks to fulfil.

Therefore, to address the shortcomings mentioned above, this research introduces a novel indicator, EC_n (Overall Efficiency of PCM), which consolidates both charging and discharging efficiencies into a single storage efficiency value calculated for the entire analysis period. Unlike existing methods that assess heating and cooling efficiencies separately or find the efficiencies only for a couple of days, EC_n provides a unified metric that captures the overall storage efficiency of PCMs for the whole simulation period, facilitating a more effective selection process for optimum PCM. This indicator (Section 2.2 of the Methodology) takes into account the actual daily charging and discharging durations of PCMs and can evaluate the latent heat stored by PCMs in multilayered constructions, which was not possible with the indicators provided by [17,18]. Furthermore, this numerical study introduces an innovative approach that simultaneously optimizes PCM performance for storage efficiency, energy efficiency, and thermal comfort in a residential ASHRAE 90.1 mid-rise apartment. EnergyPlus v8.9 with a modeling interface of DesignBuilder was used to model and simulate six warm-temperate (Cfb) European cities for the summer period from June 1 to August 31. Since a PCM with high storage efficiency may yield higher energy savings but not necessarily higher thermal comfort for occupants [18], it is absolutely necessary to determine the impact of PCM on thermal comfort. For this purpose, a new indicator, performance factor (PF), was developed following the guidelines of EN 15251 [19] for thermal comfort in residential buildings. Additionally, a cost–benefit analysis was conducted employing the static payback period for the Cfb climate zone cites by taking the energy savings obtained from integrating PCM with the temperature-controlled natural ventilation, followed by an environmental evaluation of PCM-integrated buildings that accounted for the carbon dioxide emissions associated with each fuel source used in power production. The optimal configuration—RT-25 HC combined with temperature-controlled ventilation—achieved a peak performance factor of PF = 1.0, indicating full comfort compliance across all six cities. In Paris, the setup yielded the highest summer cooling energy savings, reaching 3376 kWh. Annual carbon emission reductions totaled 2254 kg CO₂-e, and static payback periods remained well within the assumed 50-year building lifespan, based on a PCM unit cost of USD 1 per kilogram. Ultimately, this study delivers a methodology to select the optimum PCM in terms of storage efficiency, energy savings, and thermal comfort, and such a PCM is a key to improving the performance of PCM-integrated buildings.

2. Materials and Methods

2.1. Overview

Figure 1 illustrates the sequential procedure followed in this study. It starts with the development and derivation of novel indicators proposed in this research (Section 2.2). A four-story residential building selected from ASHRAE standards will be discussed in Section 2.3, followed by Section 2.4 with details of the climate zone and chosen cities. Section 2.5 discusses the properties of commercial PCMs, which will be used in this research. Numerical simulations will be performed on the selected building model for the chosen cities in the specific climate zone, which will be discussed in Section 2.6. Different ventilation strategies will be employed to obtain the best strategy to be used in conjunction with PCMs, and these strategies will be discussed in Section 2.7.

2.2. Development and Derivation of Novel Indicators

The improvement in energy and thermal performance of PCM-integrated buildings has been extensively studied by previous researchers, whereas the storage efficiency of PCM remains understudied. PCM’s effectiveness is highly dependent on its daily latent energy storage and operating time. To elaborate, a PCM integrated into the building envelope that stores less charge than its capacity or fails to discharge at night is ineffective. Likewise, a PCM that reaches its storage capacity in a comparatively shorter duration or remains solidified/liquid for a longer duration is not beneficial. Based on these two critical factors, Ramakrishnan et al. [18] formulated two indicators, cooling energy efficiency (CE) and heating energy efficiency (HE), to assess the effectiveness of PCM during charging and discharging, respectively. However, the major issue with this study [18] was that it assumed a fixed 12 h of charging and 12 h of discharging for PCM over a daily period. This assumption is too good to be true because charging and discharging rely on outdoor temperature conditions and cannot be confined to any specified period. Secondly, since CE and HE values are computed for each day, it is possible that on some days, CE values are higher than HE values, while on other days, HE values are greater than CE values. Consequently, it is inconclusive to determine the optimum PCM for the entire analysis period based on the indicators provided in the study mentioned earlier. Therefore, in this research, the efficiency coefficients for charging and discharging of PCM were computed using their factual durations, and a novel indicator is proposed in Equation (1), which represents the overall efficiency of PCM for the whole analysis period.

$${\mathrm{E}\mathrm{C}}_{\mathrm{n}}=\sum _i^n{\displaystyle \frac{{\mathrm{E}\mathrm{C}}_{\mathrm{i}}}{\mathrm{n}}}$$

(1)

In Equation (1), EC_n is the overall efficiency of the PCM, which shows the average latent heat exploitation of the PCM for the entire analysis period, n is the analysis period, and EC_i is the efficiency of the PCM for a 24-h cycle. Since PCM efficacy is equally dependent on both charging and discharging, Equation (2) can be used to calculate EC_i.

$${\mathrm{E}\mathrm{C}}_{\mathrm{i}}=\sqrt{\mathrm{C}\mathrm{C}\times \mathrm{D}\mathrm{C}}$$

(2)

where CC represents the charging efficiency coefficient, and DC represents the discharging efficiency coefficient. EC_n aggregates the two component metrics—Charging Coefficient (CC) and Discharging Coefficient (DC)—as a geometric mean: EC_n = √(CC·DC). Because the score is the square root of the product, EC_n is co-limited by the smaller term: if either CC or DC is low, EC_n drops sharply (CC = 0.8, DC = 0.2 ⇒ EC_n = 0.40; CC = 0.2, DC = 0.8 ⇒ 0.40; CC = DC = 0.8 ⇒ 0.80). EC_n = 0 whenever CC = 0 or DC = 0 and reaches 1 only when both equal 1. The formulation, therefore, favors PCM options that both charge and discharge effectively over the analysis period, without the need to assign subjective weighting coefficients. Considering that PCM charging and discharging are contingent on latent heat storage and the corresponding durations, Equations (3) and (4) can be utilized for computing CC and DC.

$$\mathrm{C}\mathrm{C}=\sqrt{{\mathrm{Q}}_{\mathrm{c}}\times {\mathrm{T}}_{\mathrm{c}}}$$

(3)

$$\mathrm{D}\mathrm{C}=\sqrt{{\mathrm{Q}}_{\mathrm{d}}\times {\mathrm{T}}_{\mathrm{d}}}$$

(4)

where Q_c = Latent charge fraction, and is given by

$${\mathrm{Q}}_{\mathrm{c}}={\displaystyle \frac{\mathrm{D}\mathrm{i}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{a}\mathrm{l} \mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}}{\mathrm{L}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{h}\mathrm{e}\mathrm{a}\mathrm{t} \mathrm{s}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e} \mathrm{c}\mathrm{a}\mathrm{p}\mathrm{a}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y}}}$$

T_c = Latent energy storage duration within a 24-h cycle, and is given by

$${\mathrm{T}}_{\mathrm{c}}={\displaystyle \frac{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{d}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \left[\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}\right]}{24 \mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}}}$$

Q_d = Latent discharge fraction, and is given by

$${\mathrm{Q}}_{\mathrm{d}}={\displaystyle \frac{\mathrm{D}\mathrm{i}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{a}\mathrm{l} \mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{d}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}}{\mathrm{L}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{h}\mathrm{e}\mathrm{a}\mathrm{t} \mathrm{s}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e} \mathrm{c}\mathrm{a}\mathrm{p}\mathrm{a}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y}}}$$

T_d = Latent energy discharge duration within a 24-h cycle, and is given by

$${\mathrm{T}}_{\mathrm{d}}={\displaystyle \frac{\mathrm{D}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{d}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \left[\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}\right]}{24 \mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}}}$$

To be able to use the proposed indicators, acquiring information about the latent heat energy stored in the PCM layer over time is crucial. EnergyPlus, however, does not provide energy stored in each construction element and reports only the total heat energy stored on a surface. Nonetheless, EnergyPlus utilizes the established experimental correlation between enthalpy and temperature as an input to consider the variation in the specific heat of PCM with temperature. Consequently, this methodology involves updating node enthalpy and specific heat capacity based on the node temperature at each time step, utilizing the experimentally determined enthalpy–temperature relationship employed by EnergyPlus. The resulting node temperatures facilitate the derivation of node enthalpy values, using the enthalpy–temperature relationship for a specific PCM. For instance, Figure 2 depicts the enthalpy–temperature relationship for the commercial PCM RT 22HC, which was used to calculate the node enthalpies from the node temperatures. Thereafter, the effective latent storage (η) of the PCM layer at a particular instant can be expressed as

$$\mathsf{\eta }={\displaystyle \frac{{\mathrm{L}}_{\mathrm{i}}-{\mathrm{L}}_0}{∆\mathrm{L}}}$$

(5)

In Equation (5), Li stands for the Instantaneous enthalpy within the temperature range of higher and lower phase-change transition temperatures, with L₀ as the specific enthalpy at the lower transition temperature and ΔL as the Latent Enthalpy of a specific PCM, all measured in kJ/kg. The effective latent storage, denoted by η, ranges from 0 (completely discharged PCM) to 1 (completely charged PCM), with values between 0 and 1 indicating an active state. The latent charge fraction (Q_c) and latent discharge fraction (Q_d) are calculated as the difference between maximum and minimum effective latent storage during charging and discharging, as expressed in Equations (6) and (7) and depicted in Figure 3.

$${\mathrm{Q}}_{\mathrm{c}}=({\mathsf{\eta }}_{\max \left(\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g}\right)}-{\mathsf{\eta }}_{\min \left(\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g}\right)})$$

(6)

$${\mathrm{Q}}_{\mathrm{d}}=({\mathsf{\eta }}_{\max \left(\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g}\right)}-{\mathsf{\eta }}_{\mathrm{m}\mathrm{i}\mathrm{n}\left(\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g}\right)})$$

(7)

The Q_c, Q_d, T_c, and T_d values were substituted into Equations (3) and (4) to derive the CC and DC values, which can then be substituted into Equation (2) to obtain the daily PCM efficiency (EC). The overall PCM storage efficiency (EC_n) for the entire analysis period can be calculated with the help of the novel indicator presented in Equation (1), and a decision regarding the optimal PCM can be made.

In addition to evaluating the storage efficiency of PCM, it is also crucial to understand its contribution to maintaining indoor thermal comfort. For this reason, the authors introduced a performance factor (PF) indicator to assess the impact of PCM on the thermal comfort of the building. PF is a measure of the percentage of hours the operative temperature was maintained between 20 and 26 °C, a favorable temperature range recommended by EN 15251 [19] for residential buildings. This PF is given by Equation (8).

$$\mathrm{P}\mathrm{F}={\displaystyle \frac{{\mathrm{N}}_{{\mathrm{T}}_{20-26}}}{{\mathrm{N}}_{{\mathrm{T}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}}}}$$

(8)

where PF represents the performance factor; N_T20-26 is the number of hours where the temperature ranges from 20 to 26 °C, and N_Ttotal is the total number of hours in the whole analysis period. A PF value of 1 signifies that the temperature remains consistently within the specified range throughout the entire duration (100% occurrence). Conversely, a value of 0 indicates that the temperature never falls within the specified range (0% occurrence). Any intermediate value between 0 and 1 denotes the corresponding percentage of time within the specified range. Equation (8) was used to determine the PCM with the highest performance among those considered in order to maintain a temperature within the range of human comfort. The so-obtained optimum PCM will be compared to the one derived from Equation (1) to determine if the optimum PCM has changed or remained the same by taking into account thermal comfort. Similarly, to evaluate the economic and environmental impact of PCM incorporation into the building envelope, the PCM must be optimized in terms of energy savings. Equation (9) was used for this purpose to obtain the PCM with the highest energy savings.

$$\mathrm{E}\mathrm{S}={\mathrm{E}\mathrm{C}}_{\mathrm{N}\mathrm{P}\mathrm{C}\mathrm{M}}-{\mathrm{E}\mathrm{C}}_{\mathrm{P}\mathrm{C}\mathrm{M}}$$

(9)

Here, EC_NPCM stands for energy consumption in the absence of PCM in the building, EC_PCM for energy consumption with PCM incorporated, and ES for energy savings in kilowatt-hours. The performance of each PCM was evaluated by Equation (9), and the one resulting in the greatest energy savings was considered optimum. Thus, for the first time in a single research, the optimal PCM will be found and compared for the best performance by taking into account the three crucial factors of energy savings, thermal comfort, and storage efficiency in PCM-integrated buildings.

2.3. Building Description

To conduct energy simulations for various cities, a suitable building is required. For this purpose, a mid-rise apartment building shown in Figure 4 was chosen from the ASHRAE prototype buildings [20]. The Pacific Northwest National Laboratory has created prototype building models in cooperation with the US Department of Energy’s Building Energy Codes Program. These models have been rigorously simulated in diverse climate zones and can be customized for global applications [21]. The four-story, mid-rise residential building has a plan area of 46.32 m × 16.91 m and a total floor area of 3130.83 square meters. It has a window-to-wall ratio of 20%, an aspect ratio of 2.74, and a story height of 3 m. Each floor consists of eight apartments measuring 7.62 m × 11.58 m and a 1.67 m-wide central corridor, except for the ground floor, which contains seven apartments and an office zone. The building was slightly modified for model simplification, and PCM is included in the envelope.

Table 1. Exterior wall composition [20,23].

Material	d [m]	λ [W/m K]	ρ [kg/m3]	Cρ [J/kg K]	R [m2• K/W]
Stucco	0.0102	0.72	1856	840
Gypsum Board	0.0159	0.16	800	1090
Insulation					1.036
PCM	0.0200	0.2	800	1200
Gypsum Board	0.0159	0.16	800	1090

and

Table 2. Roof composition [20,23].

Material	d [m]	λ [W/m K]	ρ [kg/m3]	Cρ [J/kg K]	R [m2• K/W]
Built-up-roofing	0.0095	0.16	1120	1460
Insulation					4.318
PCM	0.0200	0.2	800	1200
Metal Surface	0.0008	45.28	7824	500

detail the material types and properties for the exterior wall and roof. Additional details regarding building envelope components, infiltrations, and internal loads are provided in the cited sources [21,22] regarding the EnergyPlus simulated baseline building.

2.4. Climate Conditions

The majority of studies on PCM-integrated buildings are performed in the temperate climate zone (Zone C, as per the Koppen–Geiger climate classification), with a big share of 64% [24]. Zone C is followed by Zone B (arid climate zone) with a 21% share, while the snow climate (Zone D) and Tropical climate zone (Zone A) contribute 13% and 2%, respectively [24]. Therefore, in this research, five famous and populated cities, Paris (France), Bilbao (Spain), Hamburg (Germany), London (UK), Brussels (Belgium), and Amsterdam (the Netherlands), were selected from the warm temperate climate zone (Cfb). The summer season, from June to August, was investigated in the current study. The information regarding the chosen cities is summarized in

Table 3. A description of the chosen cities.

Climate Zone	City	Country	Latitude	Longitude	Altitude (m)
Warm temperate climate (Cfb)	Paris	France	48.8566° N	2.3522° E	35
	Bilbao	Spain	43.2630° N	2.9350° W	19
	Hamburg	Germany	53.5488° N	9.9872° E	116.2
	London	United Kingdom	51.5072° N	0.1276° W	11
	Brussels	Belgium	50.8476° N	4.3572° E	57
	Amsterdam	Netherlands	52.3676° N	4.9041° E	2

.

2.5. Characterization of Commercial Phase-Change Materials

The commercial PCM is selected from the Rubitherm RT line of PCMs. Rubitherm PCMs provide a very efficient method for thermal heat storage, even with limited volumes and little variations in operating temperature. Rubitherm PCMs offer comprehensive thermophysical properties, including partial enthalpy values, which are used to generate enthalpy curves subsequently incorporated into EnergyPlus simulations, and exhibit negligible sub-cooling, no phase segregation, and are non-corrosive, while many salt hydrates require nucleating agents and long-term stabilizers [4]. Using a chemically stable family avoids introducing aging-correction factors into EC_n and PF. Therefore, Rubitherm was selected for its reliable and well-documented data. For this research, a constant heat storage capacity of 190 kJ/kg, a specific heat capacity of 2 kJ/kg.K, and a melting range of 4 °C were used for all the PCMs considered. A PCM layer thickness of 20 mm was chosen because it is the optimum performing PCM in the warm temperate climate zone (Cfb) [25]. The chosen PCMs comprise “RT 22HC”, “RT 25HC”, “RT 28HC”, and “RT 31HC”, with “RT” representing Rubitherm company, the numerical value indicating the peak transition temperature of the respective PCM, and “HC” denoting high heat capacity. For example, RT 22HC has a melting temperature range from 20 to 23 °C with a peak melting temperature of 22 °C. Since the analysis is confined to the summer period—where operative temperatures are expected to approach the upper comfort limit—a wider melting-temperature span (22–31 °C) was selected. Figure 5 illustrates the enthalpy–temperature curves for the selected PCMs, and the key characteristics of these PCMs are outlined in

Table 4. Physical properties of Rubitherm PCM.

Name	Conductivity [W/m⋅K]	Specific heat [kJ/kg⋅K]	Density [kg/m3]	Enthalpy [kJ/kg]	Peak Melting Temperature [°C]	Melting Temperature Range [°C]
RT 22HC	0.2	2	760	190	22	20–23
RT 25HC	0.2	2	760	190	25	22–26
RT 28HC	0.2	2	760	190	28	26–29
RT 31HC	0.2	2	760	190	31	29–32

.

2.6. Numerical Simulations and Validation

The use of numerical simulations is gaining significance within the realm of energy analysis, with EnergyPlus [26] emerging as a prominent software for modeling building energy performance [8,13]. In this research, energy simulations were performed for the summer period (01 June to 31 August), and EnergyPlus v8.9 was used for the simulations with DesignBuilder [27] as the graphical user interface. The weather data for the selected cities was sourced from the Climate.OneBuilding.org (accessed on 15 September 2022) database [28] and imported into EnergyPlus. The simulation period was constrained to the designated summer timeframe, spanning from June 1 at 00:00 to August 31at 24:00. EnergyPlus offers a wide range of advanced features, including heat balance load calculations, integrated loads, and simultaneous system and plant evaluations within the same time frame. Its user-friendly interface allows for easy customization of HVAC system descriptions and straightforward data formatting for result visualization. This program also includes a wide range of choices for surface convection algorithms, sophisticated airflow calculations, environmental emissions assessment, and comprehensive economic analyses, such as energy and life cycle costs [29]. Additionally, it supports PCM and variable thermal conductivity material simulations. To include the change in specific heat resulting from the phase transition phenomenon during the simulation of PCM, the conduction finite difference approach is used in combination with the enthalpy–temperature function provided by the user. Equation (10) presents the fully implicit formulation of the heat transfer equation.

$$\mathrm{C}\mathsf{\rho }∆\mathrm{x}{\displaystyle \frac{{\mathrm{T}}_{\mathrm{i}}^{\mathrm{j}+1}-{\mathrm{T}}_{\mathrm{i}}^{\mathrm{j}}}{∆\mathrm{t}}}={\mathrm{k}}_{\mathrm{w}}{\displaystyle \frac{{\mathrm{T}}_{\mathrm{i}+1}^{\mathrm{j}+1}-{\mathrm{T}}_{\mathrm{i}}^{\mathrm{j}+1}}{∆\mathrm{x}}}+{\mathrm{k}}_{\mathrm{E}}{\displaystyle \frac{{\mathrm{T}}_{\mathrm{i}-1}^{\mathrm{j}+1}-{\mathrm{T}}_{\mathrm{i}}^{\mathrm{j}+1}}{∆\mathrm{x}}}$$

(10)

where

$$K_W={\displaystyle \frac{k_{i+1}^{j+1}-k_i^{j+1}}{2}}$$

$$K_W={\displaystyle \frac{k_{i-1}^{j+1}-k_i^{j+1}}{2}}$$

$$k_i=k\left(T_i^{j+1}\right)$$

C_p = Specific Heat of a material

ρ = density of a material

Δx = finite difference layer thickness

T = temperature

i = node being modeled

i + 1 = adjacent node to the interior of the construction

i − 1 = adjacent node to the exterior of the construction

j = previous time step

j + 1 = new time step

Δt = time step

kw = thermal conductivity for the interface between node i and node i + 1

kE = thermal conductivity for the interface between node i and node i − 1

The spacing interval between nodes is defined as a finite difference layer thickness ∆x for each material using Equation (11).

$$∆\mathrm{x}=\sqrt{\mathrm{c}.\mathsf{\alpha }.∆\mathrm{t}}$$

(11)

In the above Equation (11), c is a space discretization constant; α is the material’s thermal diffusivity, and ∆t is the time step. In the present investigation, a simulation time step of 2 min and a node discretization of 3 min were chosen for all models as recommended by [30].

The importance of numerical simulation is indisputable for analyzing the heat transfer and thermal comfort in simulated buildings; however, credibility and accuracy are contingent on their validation against empirical data and experimental measurements. Therefore, the results were compared to the experimental results of the author’s earlier work to validate the simulation work [31]. The experimental model of the lightweight concrete cubicle integrated with PCM, with a dimension of 500 mm × 500 mm × 500 mm, was modeled in DesignBuilder with identical features, construction details, and operations. Shenzhen IWEC weather data were used to match test conditions. Inside-air temperatures were monitored by T-type thermocouples (±0.3 °C accuracy) at 60 s intervals. A comparison of the data revealed a temperature variation of less than 4.5% between the indoor air temperature simulated within the DesignBuilder model and the one measured in the physical experiment. This close correspondence suggests that the model can be effectively employed for assessments of thermal performance and energy consumption. Discrepancies between numerical simulations and experimental observations are frequently attributable to idealized model assumptions and measurement uncertainties. Several studies have documented deviations of up to 5% between numerical and experimental results [32,33]. The heat fluxes from both numerical and analytical solutions were found to be in good agreement. Further details about the experimental model and its validation can be obtained from our previous works [31,34]. In addition, the numerical and analytical solutions to the Stefan Problem outlined in the work of Tabares-Velasco et al. [30] have been performed to validate the reliance of EnergyPlus in simulating the performance of PCM.

2.7. Research Case Scenarios

Four scenarios depicted in Figure 6 were evaluated in this study: the base case (Reference case), the PCM case, the PCM coupled with natural ventilation case (PCM + NV), and the combination of controlled ventilation with PCM case (PCM + CV). Since the building is a residential building, the HVAC system will be operated during the occupancy hours (18:00 to 08:00) and will maintain the temperature between the lower and upper temperature set points (20 and 26 °C, respectively) for the reference case. Additionally, relative humidity thresholds of 60% for dehumidification and 25% for humidification were applied, aligning with recommended indoor comfort standards BS EN 15251 [19] for the humidity in occupied spaces. Reference or base case means neither any ventilation is employed nor the PCM is integrated into the building envelope. For the PCM case, everything remains the same as the reference case; however, a PCM layer will be added to the building’s external walls and the roof. The third scenario is night ventilation coupled with PCM; in this case, the HVAC will be switched off from 24:00 to 06:00 h and on during the remaining occupancy period. During the night ventilation period, the windows will remain open to enable the outside air to circulate inside the building with a window opening fraction of 50%. In this study, natural ventilation was modeled using the Airflow Network (AFN) feature in EnergyPlus. The AFN framework represents a network of airflow paths connecting outdoor nodes and internal zones, with airflow driven by pressure differentials resulting from wind and buoyancy effects. Rather than requiring explicit inputs for airflow velocity or distribution patterns, this model internally calculates airflow rates based on pressure differentials, environmental conditions, and defined surface characteristics. The simulation employed the “Multizone without Distribution” control option, enabling continuous multizone airflow calculations throughout all time steps. As the building configuration did not include mechanical air distribution systems, such as fans, the AFN model provided a realistic representation of passive ventilation behavior. The last scenario is combining PCM with controlled ventilation, where the windows open only when the outside temperature is at least 2 °C less than the zone and setpoint temperatures; otherwise, they remain closed, and the HVAC is switched on. Similarly, to prevent overcooling of the space, the windows are kept closed at or below 21 °C. In this case, the HVAC system will serve as a backup to the ventilation and will only be activated when needed. The purpose of this controlled ventilation is to prevent the discomfort that may result from leaving the windows open, regardless of the outdoor temperature. The ventilation control protocols were taken from these references [15,34,35]. Using the proposed indicators (see Section 2.2), PCM’s performance and its enhancements resulting from the introduction of the various cases discussed in this section were evaluated.

2.8. Economic Analysis

The capital involved in an investment is a key factor in deciding the feasibility of any project. Thus, the integration of PCM in the building envelope can be justified if it returns the investment during its useful service life. For this purpose, a static payback period (SPP) was used in this research to check the financial viability of PCM integration. SPP is the duration it takes for an investment to recover its initial cost, and in this case, the time it takes for PCM to return the initial investment in terms of annual energy savings. The SPP is given by Equations (12)–(14).

$$\mathrm{S}\mathrm{P}\mathrm{P}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{P}\mathrm{C}\mathrm{M}}}{{\mathrm{C}}_{\mathrm{S}}}}={\displaystyle \frac{\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{o}\mathrm{n} \mathrm{P}\mathrm{C}\mathrm{M}}{\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{l}\mathrm{y} \mathrm{r}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{u}\mathrm{e} \mathrm{b}\mathrm{y} \mathrm{P}\mathrm{C}\mathrm{M}}}$$

(12)

$${\mathrm{C}}_{\mathrm{P}\mathrm{C}\mathrm{M}}=\mathrm{m}\times {\mathrm{C}}_{\mathrm{P}}+\mathrm{A}\times {\mathrm{C}}_{\mathrm{I}}$$

(13)

$${\mathrm{C}}_{\mathrm{S}}=\mathrm{E}\mathrm{S}\times {\mathrm{E}}_{\mathrm{C}}$$

(14)

In the above equations, SPP represents the static payback period in years; C_PCM denotes the purchasing and installation cost of the PCM in USD; C_S signifies the cost savings achieved by PCM in conjunction with controlled ventilation in USD per year; m represents the mass of the PCM in kilograms; C_p stands for the cost of the PCM in USD per kilogram; A is the area of PCM incorporation in square meters; C_I represents the installation cost of PCM in USD per square meter; ES indicates the energy savings obtained by PCM integration in kilowatt-hours (from Equation (9)), and E_C represents the electricity cost in USD per kilowatt-hour.

Since the windows-to-wall ratio is 20% and PCM is only incorporated into the walls and roof, 80% of the wall and the roof area was considered while calculating the area of PCM. The purchasing and installation costs of PCM were taken from the reference [25]. This research assumed two different installation costs of USD 1 and USD 5 and a price of USD 0.7/kg for PCM. The cost of electricity in London (United Kingdom) was taken from the Statista website [36], and for the rest of the cities in Europe, the costs per kWh were taken from Eurostat Statistics [37].

Finally, comparing the payback period of PCM integration with some standards is necessary to quantify the potential benefits of its incorporation. To this end, it was assumed that the average lifespan of buildings is 50 years, and SPP was evaluated in relation to this value. SPP of less than fifty years indicates that PCM investment is economically viable. Nevertheless, an exact lifespan for buildings is not universally accepted and ranges from 40 to 100 years [38,39]; thus, we adopted the widely accepted value of 50 years [40,41,42]. There remains potential for financially feasible SPP values of even more than 50 years for buildings with a lifespan exceeding 50 years. It is important to mention that SPP does not account for the maintenance cost, PCM replacement cost (if needed), time value of money, and disposal cost at the end of the useful service life.

2.9. Environmental Analysis

The construction sector is an energy-intensive sector responsible for around one-third of the world’s total energy consumption and greenhouse emissions [1]. The relationship between energy consumption and carbon emissions is quite straightforward because over 80% of the present electricity is obtained from fossil fuels, major sources of carbon dioxide emissions [43]. Coal contributes 26.5% to the global energy supply, whereas natural gas and oil each contribute 24.5% and 30.5%, respectively [43]. To produce 1 kWh of electricity, coal, oil, and natural gas produce 1001 g, 840 g, and 469 g of carbon, respectively. Therefore, any effort to reduce energy consumption by buildings will have an impact on the reduction in carbon emissions. This property associated with each fuel source is called carbon intensity of electricity generation (CI), which describes the quantity of grams of carbon produced per kilowatt-hour of electricity generated using a particular fuel source. The CI of major electricity sources is provided in Figure 7. In this study, carbon emissions from all fuel sources were considered to determine the carbon emission reductions associated with electricity savings in all the cities investigated. The contribution of a particular fuel source to the overall electricity generation in a country was obtained from “Our World in Data” [44] website and “British Petroleum’s Statistical Review of World Energy, 2022 | 71st Edition” [43]. Finally, the reduction in carbon dioxide emissions by incorporating PCM combined with controlled ventilation was calculated using Equations (15) and (16), which were proposed in our previous research article [34].

$${\mathrm{E}\mathrm{S}\mathrm{C}}_{\mathrm{i}}={\mathrm{R}\mathrm{F}}_{\mathrm{i}}\times {\mathrm{E}\mathrm{S}}_{\mathrm{P}\mathrm{C}\mathrm{M}}$$

(15)

$${\mathrm{E}\mathrm{S}\mathrm{C}}_{\mathrm{n}}={\mathrm{R}\mathrm{F}}_{\mathrm{n}}\times {\mathrm{E}\mathrm{S}}_{\mathrm{P}\mathrm{C}\mathrm{M}}$$

$$\mathrm{C}\mathrm{E}\mathrm{R}=\sum _{\mathrm{i}=1}^{\mathrm{n}}({\mathrm{E}\mathrm{S}\mathrm{C}}_{\mathrm{i}}\times {\mathrm{C}\mathrm{I}}_{\mathrm{i}})$$

(16)

where ESC represents the specific fuel source’s contribution to energy savings in kilowatt-hours; ES_PCM stands for the energy savings achieved by buildings incorporating PCM with controlled natural ventilation in kilowatt-hours (derived from Equation (9)); RF denotes the percentage of a particular fuel source in the total energy savings; CER indicates the reduction in carbon dioxide emissions in kilograms of CO₂ equivalent per year; CI represents the carbon intensity of electricity generation in kilograms of CO₂ equivalent per kilowatt-hour; i denotes any specific fuel source, and n signifies the nth fuel source.

3. Results and Discussion

3.1. Novel Optimization of the Phase-Change Transition Temperature

This section presents and discusses a thorough optimization of phase-change transition temperature for six European cities in the warm temperate climatic zone (Cfb) listed in Table 3 for case scenario 2. In this research, the optimal performance of PCM-integrated buildings was determined by simultaneously considering three key aspects: storage efficiency, thermal comfort, and energy savings achieved by integrating PCM in the building. In addition, indicators were developed to accurately assess storage efficiency (EC_n), thermal comfort (PF), and energy savings (ES) for the whole duration of this study rather than a few specific days.

Figure 8 reports the storage efficiency, performance factor, and ES achieved by each PCM for the considered cities. The PCM, which maximizes all three aspects: thermal comfort, energy savings, and storage efficiency, is regarded as the optimum PCM. Overall, the optimum PCM for all cities was RT 25HC (peak melting point of 25 °C) because it yielded higher values for thermal comfort, energy savings, and storage efficiency. This is consistent with the results of Saffari et al. [29], where the simulation-based optimization of PCM resulted in an optimum PCM of 25.1 °C for the Cfb climate zone. The authors took three cities—Berlin, Johannesburg, and Paris— from the Cfb climate zone, and the optimization yielded 25.1 °C for Paris, 24.88 °C for Johannesburg, and 24.48 °C for Berlin. In our study, the optimum PCM also has a peak melting temperature of 25 °C, which is consistent with the literature. In another study [8], Fanger’s thermal comfort was used to analyze the performance of PCM worldwide. From Cfb zones, four cities—Melbourne, Bilbao, Hamilton, and Hobart—were selected, and the optimum melting temperatures of PCM were 25 °C, 26 °C, 24 °C, and 24 °C, respectively. In our analysis, we chose PCM with melting temperatures of 22 °C, 25 °C, 28 °C, and 31 °C. From the explored literature, it is clear that our optimum PCM (25 °C) is consistent with the results of the literature for the Cfb climate zone. Furthermore, the variation in storage efficiencies of PCM and energy savings with the change in the peak transition temperature of PCM is also evident from Figure 8. Nevertheless, since HVAC ensures thermal comfort from 18:00 to 08:00 (residential occupancy period), scenario 2′s (PCM Case) performance factor is consistently 1. This indicates that the indoor operative temperature remained in the acceptable range of 20 °C to 26 °C during the occupancy throughout the entire analysis period. An intriguing observation can be made regarding the relationship between the peak melting temperature of PCM and both storage efficiency (EC_n) and energy savings (ES). Initially, when the peak melting temperature increases from 22 to 25 °C, both EC_n and ES also increase. However, above 25 °C, both values gradually decline until reaching 31 °C. This is because the HVAC cooling set point temperature is fixed at 26 °C. It does not allow the temperature to exceed this value for the occupancy period, resulting in a temperature gradient that suits RT 25HC with a peak melting temperature of 25 °C and a melting range of 23–26 °C. Therefore, RT 25HC has higher EC_n values for all the selected cities, and consequently, it is the optimum PCM with the highest ES among all the PCMs considered.

Several researchers explored the storage efficiency, thermal performance, and ES obtained by incorporating PCM in the building envelope [15,18]. For instance, Ramakrishnan et al. [18] studied the performance enhancement of a residential building integrated with PCM for Australian weather conditions and developed indicators for storage efficiency and thermal comfort. The authors devised two different coefficients for calculating PCM’s heating and cooling storage efficiencies using an ideal charging and discharging period of 12 h over a 24-h day. The limitation of this research was the inability to ascertain the optimum PCM solely based on the heating and cooling coefficients. This was because, in one PCM, the cooling coefficient had a higher value than the heating coefficient, while the converse was true for the other PCM. It is rare for both coefficients to have high values simultaneously in a single PCM. Furthermore, the assumption of twelve hours of daytime charging and twelve hours of nighttime discharging was too good to be true because PCM’s thermal storage and discharge depend on outdoor conditions and can not be confined to a particular period. In another study [15], a small office building from ASHRAE standards [20] was integrated with PCM and analyzed in 15 cities chosen from arid and temperate climate zones based on the Koppen–Geiger Climate Classification [45]. The selection of the optimum PCM was only focused on energy savings without considering the thermal comfort and storage efficiency of the PCM. Thus, this research addressed the deficiencies of previous studies and identified the most effective PCMs by taking into account all the significant aspects of PCM-integrated buildings. First, it considered the real discharging and charging of the PCM; second, it came up with one indicator of storage efficiency (EC_n) of the PCM; and third, it considered its thermal performance and energy savings. Now, the optimum PCM can be precisely determined by the newly devised indicator (EC_n), with this optimum PCM coinciding with the one that optimizes both thermal comfort and ES, as illustrated in Figure 8.

3.2. Impact on Thermal and Energy Performance by Coupling PCM with Natural Ventilation

Natural ventilation significantly influences the performance of PCM, with two distinct types based on control mechanisms and schedules. These include temperature-controlled ventilation throughout the occupancy period, known as controlled natural ventilation, and ventilation at night without temperature control, referred to as night ventilation. The ensuing subsections will examine the impact of each type of ventilation combined with PCM.

3.2.1. PCM Combined with Night Ventilation (PCM + NV)

In this case scenario, night ventilation (NV) was introduced from 24:00 to 06:00 h in the PCM-integrated building. During this interval, the HVAC system was turned off, and windows were opened to a 50% fraction, facilitating outdoor air circulation within the building. Subsequently, the HVAC system remained switched on throughout the remaining occupancy period. Figure 9 demonstrates the enhancement in PCM storage efficiency (EC_n) and energy savings (ES) with the implementation of night ventilation. However, the application of NV leads to a reduction in thermal comfort, as indicated by the performance factor (PF). The reason for such a reduction in thermal comfort, which led to the introduction of controlled ventilation, will be discussed later.

The introduction of night ventilation (NV) unequivocally enhances both storage efficiency (EC_n) and energy savings (ES) across all examined cities, as illustrated in Figure 9. In Paris, for instance, the adoption of NV results in heightened storage efficiency for all the PCMs, with the optimal PCM (RT 25HC) achieving 19% efficiency, compared to 15% in the absence of NV (Case 2). Additionally, energy savings are significantly improved for all PCMs, with the optimal PCM (RT 25HC) yielding 3408 kWh savings, surpassing the 2169 kWh in Case 2. Figure 9 demonstrates a consistent upward trend in EC_n and ES for all other cities. In a similar study by Ramakrishnan et al. [18], at an optimum night ventilation rate of four air cycles per hour, the cooling efficiency coefficients were increased by 5%, 17%, 19%, and 33% for the climate zones of Perth, Sydney, Brisbane, and Melbourne, respectively. The authors did not consider energy saving in their study; otherwise, the higher efficiency coefficients would have led to higher energy savings. In Hamburg and Brussels exclusively, an intriguing phenomenon occurred concerning the shift in the optimal PCM from RT 25HC to RT 22HC. The reduction in room temperature overnight, caused by the influx of external air, is responsible for the variation in the optimum PCM between Case 2 (PCM case) and Case 3 (PCM + NV case). This decreased temperature is ideal for the PCM with a low peak melting temperature (RT 22HC) for achieving a high storage efficiency. For example, in Hamburg, RT 22HC yields a high EC_n of 20% compared to 18% by RT 25HC; therefore, it is the optimum PCM. The change in peak melting point of PCM from a higher value to a lower value with the introduction of night ventilation is very much possible. For instance, in a study [34], the peak melting point of PCM for Frankfurt (Germany) shifted to 26 °C from 28 °C when night ventilation was introduced in the PCM-integrated building. Therefore, when integrating various ventilation strategies with the passive PCM, it is critical to consider the optimal PCM for a given city.

Nonetheless, it is crucial to prioritize the thermal comfort of the individuals living inside the building. The observed decrease in PF values from the PCM case scenario (Figure 8) to the PCM + NV scenario (Figure 9) indicates that this aspect is compromised in the PCM + NV scenario. This is because, in the PCM case, the HVAC system maintained the temperature within the setpoint limits of 20–26 °C, and therefore, thermal comfort was ensured for the whole occupancy period. Due to NV, the outside air is introduced without imposing any temperature control; the inside operative temperature can go below 20 °C and above 26 °C, depending on the outside temperature. Therefore, thermal comfort can not be ensured, although it yields less energy consumption due to fewer hours of the HVAC system working. This led to coming up with a strategy that will both ensure thermal comfort and maximize energy savings, which is combining PCM with temperature-regulated ventilation. The following section will discuss PCM combined with such controlled ventilation.

3.2.2. PCM in Conjunction with Controlled Ventilation (PCM + CV)

This final case scenario is introduced to harness the capabilities of controlled natural ventilation to improve ES and maximize PCM storage efficiency while maintaining the essential thermal comfort standards for occupants within a building. In this case, natural ventilation, with the help of operable windows, works simultaneously with the HVAC system throughout the occupancy period, and the latter serves as a backup for ventilation, activating only when needed. Figure 10 presents a comparative analysis of the energy savings (ES), storage efficiencies (EC_n), and thermal comfort (PF) achieved by the optimum PCM in the PCM + CV scenario (RT 25HC) as opposed to the PCM and PCM + NV scenarios.

Figure 10 reveals that the cities may be categorized into two groups based on their performance in the PCM + CV case compared to the PCM and PCM + NV case scenarios. The first group consists of two cities, namely, Bilbao and London. PCM + CV outperforms PCM + NV in both cities, resulting in higher ES, ECn, and PF values for the optimum PCM (RT 25HC). However, it is worth mentioning that thermal comfort, as represented by PF, was consistently achieved 100 per cent of the time in both the PCM and PCM + CV case scenarios, whereas it was compromised for all the cities in the PCM + NV scenario. The 100% comfort compliance was attributed to well-defined HVAC control strategies across both scenarios. In the PCM-only case, the HVAC system operated continuously during occupancy periods to maintain thermal comfort. In the PCM combined with controlled ventilation (PCM + CV) case, HVAC served as a backup system. Specifically, when the outdoor temperature fell more than 2 °C below the indoor temperature, windows were automatically opened to enable natural ventilation; otherwise, the windows remained closed, and the HVAC system was activated to ensure indoor conditions stayed within the comfort range. The enhanced performance of PCM + CV can be attributed to the exposure of indoor spaces to low outdoor nighttime temperatures, which promoted the solidification of PCM (placed inside the insulation material). As a result, PCM storage efficiency increases, leading to significant energy savings.

On the other hand, cities such as Hamburg, Brussels, Amsterdam, and Paris constitute the second group. In this group of cities, as shown in Figure 10, EC_n and ES achieved by the optimum PCM in PCM + CV are slightly lower than those of PCM + NV. This is because in the PCM + CV, the HVAC system runs for the whole occupancy period (18:00–08:00 h) and is switched off only when the outdoor temperature is 2 °C lower than the zone and set point temperature. While in PCM + NV, the HVAC system is turned off from 00:00 to 06:00 h, regardless of the outdoor or indoor temperature, which results in a daily reduction in HVAC usage for six hours. The ES difference between PCM + CV and PCM + NV is 31, 63, 26, and 11 kilowatt-hours for Paris, Hamburg, Brussels, and Amsterdam, respectively, even though HVAC remains operative throughout the entire occupancy period (see Figure 10). However, thermal comfort (denoted by PF) is attained for the whole occupancy period in PCM + CV. Therefore, achieving desirable thermal comfort while sacrificing a marginal amount of energy savings is a preferable option. It must be noted here that one might question why cities classified under the same Cfb climate zone exhibit differing levels of energy savings when incorporating PCMs or PCM-integrated ventilation strategies. This variation arises from site-specific factors—such as wind characteristics, sunshine duration, precipitation intensity, cloud cover, daily temperature fluctuations, humidity levels, and elevation above sea level—that are not captured by the Köppen–Geiger classification alone [29]. Therefore, while this scheme offers a useful macro-level climate categorization, it should be supplemented with consideration of these microclimatic influences during result interpretation. For example, higher altitudes generally receive greater solar irradiance, which enhances solar heat gains on building surfaces and significantly alters the system’s overall energy balance.

Based on the above discussion, it can be concluded that PCM + CV is the best strategy to be used in buildings because it gives complete thermal comfort and yields substantial energy savings. Based on the preceding discourse, it can be suggested that using controlled ventilation in buildings with phase-change material integration is the most effective approach. This combination ensures complete thermal comfort and provides significant energy savings. Hence, the subsequent sections will employ the energy savings achieved through PCM + CV to determine the foreseeable economic and environmental advantages.

3.3. Evaluation of the Economic Impact of Using PCM in the Building Envelope Combined with Controlled Ventilation

The financial viability of buildings integrated with PCM in conjunction with temperature-regulated natural ventilation was conducted in six cities situated in the warm temperate climate zone (Cfb). The static payback period (SPP) was utilized as an indicator for this evaluation. Assessing SPP in relation to the building’s service life (50 years) will ascertain the suitability of PCM incorporation into buildings; integration is considered viable if the SPP value is below 50 years. Figure 11 depicts the SPP values by considering USD 1 and USD 5 installation costs.

It can be seen from Figure 11 that for the installation cost of USD 1, PCM coupled with controlled ventilation yields SPP values of less than 50 years for all the cities considered. However, when considering the installation cost of USD 5, the SPP values for Bilbao, London, and Brussels are below 50 years, while those for Paris, Hamburg, and Brussels are marginally above 50 years. Despite payback periods slightly exceeding 50 years for the USD 5 PCM installation cost, investing in PCM integration in Paris, Hamburg, and Brussels remains worthwhile due to two factors. First, the energy savings obtained in this analysis are only for the summer period (June 1 to August 31), and energy savings for the remaining months are not taken into account. Incorporating a PCM into the building envelope can yield energy savings for the remaining months of the year as well [29]. The additional energy savings may reduce the SPP significantly below 50 years. Second, in this research, the useful life of buildings is assumed to be 50 years; however, there is no consensus on a single useful life of buildings, and it generally ranges from forty to a hundred years [38,39]. PCM integration can still be feasible even with a higher value of SPP (more than 50), depending on the actual service life of the building.

Furthermore, it is worth mentioning that the SPP for PCM-integrated buildings depends on the region’s energy savings and electricity prices. For instance, Paris yields energy savings of 3376 kWh, while Amsterdam yields only 1691 kWh; however, Amsterdam has low payback periods of 33 and 44 days for USD 1 and USD 5 installation costs, respectively. This is because of the difference in the unit cost of electricity in these cities. The unit cost of electricity in Paris is USD 0.202/kWh, while it is USD 0.475/kWh (almost double) in Amsterdam [37]. Consequently, the worth of electricity savings in Amsterdam is higher than in Paris, resulting in low payback periods, as shown in Figure 11.

Overall, PCM and its combination with controlled natural ventilation are economically feasible for the warm temperature climate zone. Nonetheless, due consideration should be given to the unit electricity costs and installation costs of PCM before its incorporation.

3.4. Influence of PCM Integration with Controlled Ventilation in the Building Envelope on the Environment

The global cooling demand is anticipated to increase due to population growth and climate change, as stated in the “World Energy Outlook, 2022” [2]. Currently, a mere one-third of households are equipped with air conditioners, and this number is also expected to increase owing to improved lifestyle and economic growth. To add to the problem, more than 60% of the total electricity generated is from fossil fuels and was responsible for around 13 gigatonnes of CO₂ emissions in 2021 [2]. Consequently, a reduction in energy consumption directly affects carbon emissions reduction (CER). Moreover, carbon emissions from different fuel sources have different carbon intensities (CI) of electricity generation. Bearing this in mind, the present research considered the CI associated with each fuel source involved in production and quantified the amount of CER for a building incorporating PCM and controlled natural ventilation. Figure 12 illustrates the energy savings obtained in each city analyzed and the corresponding reductions in carbon emissions.

It can be seen from Figure 12 that for higher energy savings in Paris and Bilbao, the carbon emission reduction values are also higher, showing a direct relationship. Nonetheless, this direct relationship is not always true because CER depends on both energy savings and the source of electricity. A city with lower energy savings (ES) may still be able to achieve a higher CER. This can be witnessed in Figure 12, where cities with lower ES have higher CER. For instance, Hamburg has a high CER value of 1191 kg CO₂ e/year compared to 1043 in London; however, the ES in London (2062 kWh) is much higher than that in Hamburg (2013 kWh). This disparity is due to the different sources of electricity in these cities, as illustrated in Figure 13a. While both Hamburg and London rely on fossil fuels for a similar proportion of their electricity generation (76% in Hamburg and 74% in London), Hamburg stands out by obtaining 19% of its electricity from coal, which is the primary contributor to carbon emissions with a CI value of 1. In contrast, coal only accounts for approximately 3% of London’s electricity generation. Therefore, London has a higher CER value than Hamburg. Similarly, the same comparisons exist between Amsterdam and Brussels, as shown in Figure 12, where Brussels has a slightly higher ES but lower CER compared to high CER and low ES values in Amsterdam. Figure 13b presents a comparison of electricity sources for Brussels and Amsterdam. It is evident from the comparison that Amsterdam relies more heavily on coal, natural gas, and oil for its electricity generation than Brussels. Therefore, it has a higher CER despite a slightly lower ES.

In conclusion, it can be affirmed that the integration of PCM in a building, coupled with controlled natural ventilation, yields significant carbon emission savings of up to 2254 kg carbon dioxide equivalent per year in the warm temperate climate zone (see Figure 12). Furthermore, the potential benefits of this technology are influenced by both the energy savings and the sources of electricity in a region.

4. Conclusions and Recommendations

This study details a method for selecting the most effective PCM (optimum PCM) that maximizes the energy efficiency and thermal comfort of buildings, yielding significant economic and environmental benefits. Novel performance indicators were devised to assess PCM storage efficiency (EC_n), thermal comfort (PF), and energy savings (ES). Through a case study involving a mid-rise residential building integrated with Rubitherm PCMs, this paper proposes an optimized PCM layer design, considering variations in phase transition temperature and the implementation of night ventilation and controlled natural ventilation. The quantitative assessment of the proposed indicators from simulation results facilitates a comprehensive evaluation of PCM behavior, revealing an optimal PCM configuration that maximizes energy efficiency, thermal comfort, and storage efficiency. Key findings include the following:

• The proposed indicators enable the evaluation of PCM storage efficiency by quantifying its active engagement with latent energy during actual charging and discharging cycles. This approach provides meaningful insights into PCM’s operational performance under distinct climatic conditions;
• PCM thermal performance was assessed using the performance factor (PF), aligned with BS EN 15251 guidelines. This approach effectively quantifies the indoor thermal comfort achieved through PCM integration;
• Optimal integration of PCMs within building envelopes requires a balanced consideration of storage efficiency, energy savings, and indoor thermal comfort. This integrated strategy enables the simultaneous enhancement of latent heat utilization, occupant comfort, and overall energy performance—offering a practical pathway toward high-efficiency PCM-enabled designs;
• The novel indicators introduced for assessing storage efficiency and thermal comfort proved effective in identifying the optimum PCM. Their reliability was further validated by comparing the selected PCM to the one delivering the highest energy savings in each city, confirming the indicators’ utility for climate-specific PCM selection;
• PCM with a peak melting point of 25 °C (RT 25HC) was found to be optimum for all the cities analyzed in the warm temperature climate zone (Cfb);
• The implementation of night ventilation in the PCM-integrated building significantly improves energy savings and PCM storage efficiency. Nevertheless, the thermal comfort of the occupants might be compromised, contingent upon the prevailing outdoor conditions;
• The drawback of compromised thermal comfort in the case of night ventilation can be addressed by implementing temperature-controlled natural ventilation, which improves energy savings and storage efficiency of PCM while ensuring optimal indoor comfort conditions;
• The PCM combined with controlled natural ventilation yields considerable economic benefits with a static payback period of less than 50 years (the service life of buildings) for all cities, considering a USD 1 installation cost, and slightly over 50 years for a few cities with a USD 5 installation cost. Furthermore, the economic benefits were found to be dependent on energy savings and the cost of electricity in a country. Nonetheless, future studies must consider the time value of money, appropriate discount rates, and the replacement cost of PCM during the service life of the building;
• PCM, in combination with controlled natural ventilation, results in significant carbon dioxide emission reductions (CER) of up to 2254 kg of carbon dioxide equivalent per year in the Cfb climate zone. In addition, it turns out that the CER values depend on energy savings and the sources of electricity used to generate electricity in a country. However, future studies must consider the embodied carbon in the production, end-of-life disposal impacts, and interactions with evolving renewable energy grids.

Overall, the proposed method can be implemented during either the preliminary design phase, where it can be utilized to select suitable properties, or during the operational phase, where it can be employed to assess the practical efficacy of a PCM installation. However, for future studies, this approach could be expanded across multiple climate zones and building typologies. Comprehensive validation through experimental data is recommended, including the coupling of PCM systems with various ventilation strategies. To further strengthen the method’s applicability, future work could integrate dynamic payback period analysis, accounting for the time value of money, alongside full life cycle assessment (LCA) and life cycle cost assessment (LCCA).