Passive Energy Conservation Strategies for Mitigating Energy Consumption and Reducing CO₂ Emissions in Traditional Dwellings of Peking Area, China

Abstract

Within China, brick dwellings stand as archetypal relics of traditional habitation, embodying a “living fossil” status. The sustainability of these dwellings is contingent upon the integration of energy-conservation strategies. This study scrutinized and empirically assessed a representative dwelling in the Peking area. Using numerical simulations, the impact on energy consumption of factors such as insulation and glazing type, external wall thickness, insulation thickness, and solar energy utilization was evaluated. The outcomes reveal that introducing external thermal insulation—specifically, expanded polystyrene panels with a thickness of 60 mm and 40 mm for the roof and exterior walls, respectively—along with a sunspace of depth 1.5 m yielded superior energy efficiency. Additionally, substituting conventional roofing with solar tiles exhibited a potential annual electricity generation coupled with an annual solar radiation conversion efficiency of 17%. Collectively, these strategies induced a substantial reduction in annual energy consumption. This study presents tailored energy-conservation measures and provides design decision support for architects’ practical recommendations on thermal environment control of passive traditional dwellings in the Peking area.

1. Introduction

1.1. Previous Studies

Numerous research and analytical studies have consistently indicated that energy consumption associated with buildings comprises more than 30% of global energy consumption, a figure that escalates to approximately 35–40% within developed nations [1]. To specify, a significant proportion of energy, ranging from 30% to 60%, is dedicated to enhancing the indoor thermal conditions within buildings [2]. Over the last 15 years, China has witnessed a 2.24-fold increase in its overall carbon emissions from the construction sector, demonstrating an average annual growth rate of 5.92%, surging from 2.234 billion tons of CO₂ to 4997 million tons of CO₂ [3]. Given the swift expansion of China’s economy, it becomes imperative for the Chinese government to exercise control over internal emissions and curtail building energy consumption to a maximum threshold of 1.1 billion tons of standard coal (representing 23%). This effort is not only vital for ecological conservation but also crucial for maintaining a healthy and comfortable indoor environment [4,5,6].

In confronting the multifaceted challenges of global climate warming and energy crisis, it becomes imperative to magnify our focus on energy conservation embedded within architectural paradigms. Buildings, notably dwellings, command a significant portion of global energy consumption, illuminating a tangible trajectory for enhanced energy streamlining and diminished environmental impact. While much of the discourse gravitates towards the sleek silhouettes of modern skyscrapers and the prowess of technologically advanced edifices, the traditional folk homes of northern China, with their profound architectural lineage, beckon for commensurate contemplation. Northern Chinese dwellings, with their robust brick masonry, courtyards, and intricate woodwork, reflect a deep architectural legacy. Designed for a region marked by cold, dry conditions, these homes emphasize heat conservation and wind protection. The severe winters highlight the need for energy efficiency. Given this architectural heritage, it is essential to harness their energy potential, aligning with global sustainability goals and maintaining their relevance in a modernizing world.

The global push towards sustainability and energy conservation has been rapidly gaining traction in the field of architecture and urban planning. Numerous studies have delved into the energy-efficient retrofitting of traditional dwellings, with a primary focus on enhancing the performance of existing architectural materials. Among these, research on passive retrofitting for Tibetan traditional residences has pinpointed potential passive modifications, underscoring the paramount importance of cultural and architectural preservation [5]. The energy performance of new Yaodong dwellings in China’s Zaoyuan village showcases the efficacy of traditional heavy envelopes combined with improved insulation techniques, resulting in significantly reduced energy consumption [7]. The concern for preserving historical features while improving energy performance is evident in Korea’s modern historic buildings [8]. Using specialized retrofit technologies, such as vacuum insulation panels (VIP), the study highlights an effective method to enhance energy performance without compromising historical integrity. Concurrently, EU countries are recognizing the potential of retrofitting attic spaces to maximize urban density without the necessity of new construction [9]. Here, reflective insulation and low emissivity paint treatments demonstrate the potential to alter the thermal behavior of roofs. A study has been conducted to greenify traditional dwellings in the cold region (Zone B) of central Hebei. This study involved research on typical traditional dwellings, and quantified energy-saving measures were derived through optimization. The research findings recommend an EPS insulation layer thickness of 100 mm for the roof and 60 mm for the external walls. However, it is important to note that this study exclusively analyzed modern residential buildings in the area and did not take historical structures into account [10].

On the other hand, solar energy utilization stands as a pivotal topic in residential energy conservation. Notably, the use of passive solar energy offers novel strategies to enhance thermal comfort and energy efficiency in addressing the energy concerns of traditional homes [11,12]. One such strategy involves the addition of sunrooms on the southern side of houses to mitigate heat loss during winter and optimize solar utilization, catering to both energy conservation and thermal comfort needs [13,14,15,16]. Research has been initiated on the photovoltaic potential and feasibility in areas sensitive to natural and heritage concerns. When assessing rooftops’ photovoltaic potential with heritage compatibility in mind, there is a discernible reduction in conversion efficiency. However, there is a notable decrease in overall carbon emissions, and the investment return rate is promising. Further, studies on rooftop photovoltaic technologies for new residences in developing countries indicate that smaller solar panels offer greater adaptability [17]. Overall, comprehensive research on northern Chinese brick dwellings remains under-explored, especially considering their unique heritage value and climatic characteristics. It suggests a notable research opportunity for exploring their potential in energy conservation and photovoltaic applications.

1.2. Motivation

In northern China, including Beijing, Hebei, Shanxi, and Tianjin, traditional brick-built dwellings are quite common. The materials and forms of these traditional brick-built dwellings are similar, and research on energy-efficient renovations of traditional brick-built dwellings in the Beijing region can be effectively applied and replicated elsewhere. These brick residences of northern China epitomize the essence of Chinese culture. They embody a specific type of housing, characterized by local representation and distinctiveness, largely conceptualized and constructed autonomously by the native inhabitants. However, with the ongoing surge of modernization and rapid urban development, many of these brick homes fall short of contemporary living comfort standards, teetering on the verge of abandonment or substitution. Due to the obsolescence of materials and techniques, their innate energy conservation benefits may be compromised in today’s setting. It is, therefore, pivotal to rejuvenate these buildings, ensuring their historical and cultural significance remains intact while meeting contemporary living and energy-efficiency criteria. The following questions then arise:

• · What strategies can enhance the thermal efficiency of these traditional brick houses while preserving their architectural integrity?
• · Given the abundance of solar radiation in the Peking region, how can we effectively harness this resource to reduce indoor energy consumption?
• · How much energy efficiency can these strategies offer, and how economically feasible and implementable are they?

This study seeks to address these knowledge gaps by offering a thorough examination of the thermal and energy consumption dynamics of traditional brick dwellings in Peking. It also introduces energy-efficient renovation strategies for traditional brick houses in Beijing, considering both economic and environmental factors, marking a significant step in this direction. Further material is divided into several parts. Thus, in Section 3.4.1, we delve into the impacts of three strategies—envelope structure optimization, solariums, and rooftop solar energy generation—on indoor comfort and energy efficiency. Section 3.4.2 presents the examination of the carbon emission rates of these strategies, as well as the energy conversion rates of solar tiles made of different materials, comparing photovoltaic panel materials with electricity consumption. Finally, in Section 3.4.3, the study explores the investment payback period from an implementation standpoint. This research aims to enhance the understanding of architectural energy conservation for Peking’s traditional brick houses, providing valuable data support for local governments and researchers interested in photovoltaic systems for northern brick residences.

2. Materials and Methods

2.1. Geographical Location and Climatic Characteristics

The subject of this study is a brick-and-tile house located in Cuandixia Village, Mentougou District, Peking, China. Mentougou District is situated to the west-southwest of central Peking, between longitudes 115°25′00″ and 116°10′07″ as well as latitudes 39°48′34″ and 40°10′37″. The region is a transitional area from the North China Plain to the Mongolian Plateau, with elevations higher in the northwest and lower in the southeast. Figure 1 displays the location, layout, and surroundings of the residence. The district experiences a mid-latitude continental monsoon climate: dry and windy springs, hot and rainy summers, cool and humid autumns, and cold and dry winters. The annual average high temperature is around 20 °C, with an average low of 9 °C. The frost-free period lasts about 200 days annually, with ample sunshine averaging 2470 h per year. Precipitation peaked at 970.1 mm in 1977 and hit a low of 377.4 mm in 1997, averaging about 600 mm annually.

As shown in Figure 1, Cuandixia Village, where the brick-and-tile house is located, is a traditional Chinese village at an altitude of 650 m, covering an area of 5.33 square kilometers. Built along the mountain contours, the village sprawls out fan-shaped on both sides. It boasts 74 sets of Qing Dynasty residences, totaling 689 rooms. These historic homes are invaluable cultural relics, designated as the sixth batch of national key cultural heritage sites in Peking. They play a significant role in the study of China’s architectural history.

2.2. In-Field Experimental Campaign

The measurements for this experiment were conducted throughout the year. We collected data on both indoor and outdoor air temperatures, humidity, wind speed, and solar radiation. The setup of the measurement instruments is depicted in Figure 2. We considered the room’s practical applications and its frequency of occupation.

Table 1. Dwelling character.

Walls	Doors and Windows	Roof
Considering the construction materials employed, the walls are primarily composed of brick, with a thickness of 370 mm. These walls serve a dual purpose: they offer insulation while also promoting ventilation. A plaster coat completes the exterior finish of the wall.	One side of the courtyard is designated as the front. Besides a brief segment of the windowsill wall, the majority of this facade features wooden doors and windows. All rooms within the compound have their doors and windows oriented towards the inner courtyard. The exterior and lateral walls are constructed robustly, often devoid of windows or furnished with only diminutive ones. This architectural approach compromises the building’s air-tightness, considerably impacting the indoor climate during winter.	Each roof features a double-sloped design reminiscent of a sharp mountain peak, ensuring optimal drainage efficiency. A buffer layer between the roof and ceiling effectively mitigates the notable temperature fluctuations between winter and summer, thereby elevating indoor comfort. The roof is constructed with petite green tiles, known for their durability and reduced long-term maintenance costs. Furthermore, these tiles exhibit exceptional insulation and waterproofing properties.
Item	construction	U-value (w/m2)
Roof	20 mm tile	1.02
Ground	100 mm plain concrete	2.92
Windows	Wooden frame single glazing (3 mm)	6.257
Brick dwelling	200 mm brick	0.72
Interior wall	30 mm Wooden Board	5.621

provides detailed specifications of all the measurement equipment utilized, ensuring compliance with the GBT 50785-2012 standard. Temperatures and humidity levels, both indoor and outdoor, were measured at a height of 0.6 m, centered within the room and the courtyard respectively. For spaces less than 16 square meters, special consideration was given due to their size. Given the room’s layout, outdoor temperature measurements were solely taken from the southern direction. For a comprehensive layout of the testing points, refer to Figure 2.

2.3. The Characteristics of Brick Dwellings

Brick dwellings in the region have prominently evolved by harnessing local materials from the neighboring mountains. Residents meticulously shaped these large stone slabs, incorporating them into the foundation, walls, roofs, and other structural parts of the dwellings based on specific requirements. Additionally, the village’s roads, stairs, and various public amenities have been constructed from these stone slates. Compared to other rural housing structures, brick dwellings provide notable thermal benefits—ensuring warmth during winter and a cooling effect in the summer. Table 1 details the distinctive features of these brick dwellings.

2.4. Numerical Analysis

2.4.1. Simulation Model Setting and Validation

Cuandixia Village, nestled in the central region of Jaitang’s northwest narrow valley, is a traditional village situated to the west of Peking. With an altitude of 650 m, the village spans an area of 5.33 square kilometers. As mandated by relevant regulations, historical structures must preserve their original height, mass, external facade, and color. Any retrofitting initiatives should be undertaken with expert guidance and in collaboration with local authorities. The ensuing strategies have garnered the endorsement of both residents and experts [18].

Given the architectural functionalities, the indoor thermal conditions of the slate dwelling can be augmented via pertinent improvement measures. This not only fosters energy conservation but also aligns with the thermal comfort preferences of the local inhabitants [19].

The Design Builder-7.0.2.006 software was deployed for strategy validation and numerical simulation, leveraging the Energy Plus engine—a tool frequently recognized in academic circles for its precision [20]. This study pinpointed optimization techniques through quantitative simulations and then authenticated the energy consumption enhancements annually under both heating and cooling conditions. Additionally, improvements in the indoor thermal environment during periods without heating or cooling were verified for the ultimate optimized solution [21]. The model’s accuracy has been substantiated both by simulations and preceding studies conducted by our team [2,18,19,20,21,22].

While the simulation program’s reliability has been previously affirmed, its precision was further scrutinized by juxtaposing it with prototype measurements, especially given the intricacies of the solar system [23]. To ensure output accuracy, identical prototype characteristics were meticulously incorporated into the computerized model [24,25,26]. Concurrently, Root Mean Square Error (RMSE) was employed to discern the alignment between values simulated using standard climate files obtained from the Energy Plus official repository and empirical values. The governing equations are delineated in Equation (1).

$$\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=\frac{1}{{\overline{E}}_{mea}}\sqrt{\frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{M}}_{\mathrm{s}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n},\mathrm{i}}-{\mathrm{S}}_{\mathrm{m}\mathrm{e}\mathrm{a},\mathrm{i}}\right)}^2}{\mathrm{n}}}$$

(1)

where M_simulation,i represents the data simulated at the experimental point, E_mea,i represents the data measured at the experimental point, $\overline{E}$_mea represents the mean measured data of all selected points, and n represents the number of detections.

2.4.2. The Parameter Settings of the Improvement Strategy

Simulation results, depicted in Figure 3, reveal that the building’s exterior walls, roofs, and windows serve as primary conduits for heat exchange. Thus, enhancing the insulative properties of these structural components offers a promising avenue for energy conservation. Additionally, we aim to incorporate solar housing designs, introduce solar tile roofing, and employ other techniques to augment the available energy for the residential area.

• Enhancement of Openings

The redesigned openings aim to mitigate the effects of prevailing winter winds on the bedroom, thereby reducing indoor heat loss. Additionally, this design improves natural lighting and ventilation. Incorporating glass with superior insulation properties, as outlined in

Table 2. Glazing type.

Glazing NO.	Window Glazing Type	Total Solar Transmission	U-Value (W/M2K)	Light Transmission
G1	Single clear (6 mm) (SC)	0.81	6.1	0.744
G2	Sgl LoE (e2 = 0.2) Clr 6 mm	0.72	3.779	0.811
G3	Dbl LoE (e2 = 0.1) Tint 6 mm/13 mm Air	0.38	1.761	0.44
G4	Trp LoE (e2 = e5 = 0.1) Clr 3 mm/13 mm Air	0.474	0.982	0.661
G5	Single reflective (6 mm) (SR)	0.28	5.1	0.72
G6	Double reflective-D (6 m/13 mm Air) (DR)	0.7	2.7	0.69

, further elevates indoor thermal comfort.

2.

Optimization of the Enclosure Structure

The building’s envelope structure is vital in optimizing its indoor thermal energy consumption. The brick dwelling’s external walls and roof currently lack sufficient thermal insulation, compromising the indoor thermal environment. The internal surface temperature within the dwelling primarily hinges on the heat transfer properties and thickness of the insulating material utilized in the structural layer. Ideally, materials with minimal heat transfer coefficients and increased thickness would enhance the indoor thermal environment.

As illustrated in Figure 4, the proposed renovation strategy for the external wall and roof involves augmenting an external insulation layer of a specified thickness. Through careful architectural design, the airtightness of the external wall can be enhanced. Detailed parameters of this insulation layer can be found in

Table 3. Insulation type of roof and external wall.

NO.	Insulation Type	Heat Conductivity W (m·k)	Specific Heat Capacity KJ/(kg·K)	Density (kg/m³)
I1	Woods—fir, pine	0.12	1380	510
I2	Extruded polystyrene (XPS)	0.03	1400	35
I3	Aluminum Foil	0.22	2700	130
I4	Insulation Paint	0.03	896	1240

.

• Winter Heating Scenario

In this scenario, the building is constructed to be airtight, allowing air exchange solely through infiltration. The interior air temperature is managed by the heating system. The integration of various insulation materials, as illustrated in Figure 4, fosters a more thermally accommodating indoor environment.

• Summer Mixed Cooling Scenario

During this period, the buildings adopt a hybrid approach that combines active cooling with natural ventilation. Specifically, if the external air temperature drops below the indoor temperature but remains above the natural ventilation threshold (12 °C in this study), natural ventilation is employed to cool the interior. If this cooling is insufficient, an active cooling system intervenes to maintain the indoor temperature setpoint at 26 °C. Natural ventilation rates are influenced by pressure differences at openings due to wind or stack effects, window opening sizes, and the schedule of window operations. The Energy Plus Airflow Network model provides hourly airflow rates and pertinent thermal environment parameters. By effectively leveraging outdoor air in this mixed-mode operation, cooling energy consumption is minimized.

• Heating and Cooling Systems

The primary fuel for heating is electricity, boasting a heating system coefficient of performance (COP) of 2.8. Auxiliary energy, used by pumps, which consumes 3.26 kWh per unit building area. Cooling is facilitated by a constant volume direct expansion system powered by electricity, with a COP of 3.8. Setpoint temperatures for heating, cooling, and ventilation are 16 °C, 26 °C, and 12 °C, respectively. All indoor areas are subjected to heating or cooling, except the sunspace of the newly constructed brick dwelling.

3.

Integrated Sunspace

As illustrated in Figure 5, the sunspace, connected to the main building, warms the interior in two primary ways:

(1)

By facilitating the transfer of warm air into the internal spaces via natural convection through shared windows and doors.

(2)

By augmenting the heat transfer resistance between indoor spaces and the external environment.

However, this study does not model the natural convective heat transfer, mainly because of the bidirectional airflow in the natural ventilation mode. Natural convection arises whenever a temperature disparity exists between the sunspace and internal areas. Properly regulating window and door openings to ensure they are operational only when the sunspace is warmer than internal spaces is challenging. If the sunspace is cooler, natural convection may inadvertently reduce the warmth of interior spaces. In many instances, particularly during winter heating, the internal space’s temperature, managed by the heating system, often surpasses that of the sunspace. As a result, heat may be lost through natural convective exchanges. In winter, the sunspace is considered an attached room with no convective heat transfer to the interior. During the mixed cooling scenario in summer, the sunspace is naturally ventilated, exempt from air conditioning.

4.

Roof solar energy utilization

Building Integrated Photovoltaic (BIPV) systems and Building Integrated Solar Thermal (BIST) systems integrate photovoltaic modules directly into building envelope components, such as roofs or façades [27,28,29]. This study favors solar tiles for solar energy harvesting primarily because they preserve the original architectural aesthetics of the structure.

Buildings harnessing renewable energy sources alleviate the burden on conventional energy generators and often contribute to reduced greenhouse gas emissions. To fully harness available resources, multiple systems are typically combined. This synergy has culminated in a novel technology that merges photovoltaic with solar thermal capabilities: BIPV/T. Before implementing these systems, careful consideration of the building’s location and orientation is essential; shading impacts on photovoltaic or solar thermal arrays can significantly affect energy yields.

In this research, BIPV tiles either replace all or a portion of traditional roofing tiles. These are typically assembled in modular configurations, presenting a viable retrofitting solution for roofs. The design and form of these tiles can vary—some mimic the appearance of curved ceramic tiles, which, due to their curved structure, might not provide optimal surface efficiency, as depicted in Figure 4b and Figure 6. We modeled four solar energy harnessing methods using solar tiles in place of the original small green roof tiles over an area of 66.68 m² at a 30° inclination. The specifications of these solar tiles are detailed in

Table 4. Solar photovoltaic panel type.

Photovoltaic NO.	Solar Photovoltaic Panel Type	Module Current at Max Power (A)	Module Voltage at Max Power (V)	Short Circuit Current (A)	Open Circuit Voltage (A)
P1	ASE 300-DFG/50	5.6	50.5	6.2	60
P2	BP Solar 380	4.55	17.6	4.8	22.1
P3	Shell S115	4.2	26.8	4.7	32.8
P4	Uni Solar PVL-128	3.88	33	4.8	47.6

.

2.5. The Net Present Value (NPV)

The Net Present Value (NPV) serves as a dynamic instrument employed to evaluate the feasibility of the PV project. When NPV is greater than zero (NPV > 0), it signifies that the projects will yield a gain if they meet the stipulated criteria for benchmark yield. In simpler terms, an investment in this project is economically viable. The calculation of NPV is performed as follows: Equation (2):

$$\mathrm{N}\mathrm{P}\mathrm{V}=V_0+\sum _{t=1}^n\frac{V_t}{{(1+d)}^t}$$

(2)

The formula for calculating the Net Present Value (NPV) involves several key components: V₀ represents the cash flow value at present, Vt signifies the cash flow value at the end of period “t,” “n” denotes the number of periods, which, in this project, corresponds to the warranty period of the PV system (25 years)—maintenance, funded by the owner, becomes necessary after more than 25 years of use, and its actual operational lifespan may surpass 35 years, and finally, “d” represents the discount rate, set at 2.5% in this study.

In this research, we have chosen to incorporate the Net Present Value (NPV) as a financial analysis tool specifically within the context of photovoltaic (PV) system studies. This decision is informed by the well-established and cost-effective nature of maintenance structure renewal technology, making NPV a suitable means to assess the economic viability of various PV systems.

2.6. Sensitivity Analysis

2.6.1. Approach and Method

The sensitivity analysis aimed to discern the model’s response, as simulated by Energy Plus, to fluctuations in design factors and their ensuing effect on thermal comfort. Broadly, this analysis illuminates which combination of factors profoundly influences building performance. Sensitivity analyses can be categorized into local and global sensitivities [30]. Given that modifications in one parameter’s output could concurrently influence other parameters, a global sensitivity approach was adopted for this study, comprehensively accounting for the mutual interactions and influences of all design variables. Regression analysis further elucidated parameter sensitivity, identifying primary influencing variables. To conserve computational resources and expedite processing, the Monte Carlo analysis frequently employed in thermal comfort studies was adopted [31].

2.6.2. Sampling and Statistics

Three prominent sampling techniques are associated with Monte Carlo analysis: simple random sampling, stratified sampling, and Latin Hypercube Sampling (LHS). This study adopted the LHS method, grounded in the principle of stratification. This technique ensures an equitable division of the sample space, effectively preventing redundancy within specific regions. The LHS procedure involves the following:

(1) Evenly splitting the [0,1] interval into 200 distinct sub-intervals. (2) Conducting independent random sampling within each sub-interval, ensuring only a single random value is drawn per interval. This process yields a set of 200 random values corresponding to each parameter. (3) Constructing a random matrix comprising 200 rows and 10 columns by amalgamating these random values. (4) Mapping each random value to its respective range to obtain the authentic parameter values, resulting in a matrix of these genuine values. (5) Formulating the final sample set of input parameters.

Given that the recommended LHS sample size typically ranges between 1.5 and 10 times the total input variables [32], this research initially formulated 100 LHS samples. However, to bolster the precision and validation of the sensitivity analysis, 200 LHS samples were eventually utilized. The integrated sampling tools of designbuilder facilitated the generation of data sets for the sensitivity analysis.

In terms of sensitivity measurements, both the Standardized Rank Regression Coefficient (SRRC) and the Partial Rank Correlation Coefficient (PRCC) were employed. The SRRC evaluates the linear influence of design parameters, allowing for the isolation of mutual effects among independent variables and a direct assessment of the impact of independent variables on dependent ones. In contrast, the PRCC can disentangle the interrelationships between independent variables. A higher SRRC value indicates an increased sensitivity of the parameter: a positive SRRC signifies that an increase in the parameter corresponds with a rise in the dependent variable’s value, while a negative SRRC denotes the opposite.

2.6.3. Independent Variables and Predictor Parameters (Energy Saving)

Drawing from pertinent literature [33,34] and the attributes of the chosen building, this study shortlisted nine design variables for sensitivity scrutiny. These encompassed an array of continuous, uniform, and discrete variable types, each characterized by diverse values and materials.

Table 5. Input variables for sensitivity analysis.

Category	Building Parameters	Unit	Variable Names	Probability Density Functions	Sampling Range	Variation Step
Thermal insulation	Insulation type of wall Insulation type of roof	-	X1 X2	Discrete Discrete	I1, I2 I3, I4	Table 1 Table 1
Thermal mass	Insulation thickness of the roof Insulation thickness of the wall	mm mm	X3 X4	Continuous uniform Continuous uniform	[0,45] [0,200]	5 20
Thermal mass	Thickness of roof Thickness of wall	mm mm	X5 X6	Continuous uniform Continuous uniform	[200] [200,2000]	- 200
parameters	Glazing type	-	X7	Discrete	[G1, G4]	Table 3
Type of PV system	Long axis azimuth	-	X8	Discrete	[P1, P4]	Table 4
Sunspace structural	Width	m	X9	Continuous uniform	[1.2, 1.8]	0.1

delineates these design elements, with subsequent details for discrete variables exhibited in Table 2, Table 3 and Table 4. Predominantly, the predictive mean vote (PMV) [35] serves as a reliable metric for forecasting indoor thermal comfort, validated for both naturally ventilated and air-conditioned edifices. However, certain studies suggest its potential underestimation of discomfort in colder settings [15,36]. The Design Builder-7.0.2.006 software, facilitating parametric studies, was employed to streamline simulation runs for each architectural design. This tool, interfacing seamlessly with Energy Plus, can dynamically tweak parameters within a building model, executing performance simulations. Post-simulation results were integrated into the Statistical Package for the Social Sciences (SPSS) for a nuanced sensitivity analysis.

3. Results and discussion

3.1. Field Experiment Result

Figure 7a illustrates notable outdoor temperature oscillations observed during the summer season, ranging from 15.3 °C to 33.7 °C. In stark contrast, the indoor temperature remains remarkably stable, exhibiting variations within a narrower range of 21.7 °C to 25.2 °C, with an average of 23.4 °C. It is noteworthy that the mean relative humidity during the summer period is depicted as 80%, as depicted in Figure 7a. Meanwhile, Figure 7b brings attention to the substantial fluctuations in outdoor relative humidity, which vary between 75% and 90% throughout the summer season. These fluctuations are concurrently accompanied by varying wind speeds ranging from 0 m/s to 4 m/s. The empirical data suggest that, despite the relatively favorable outdoor temperature conditions during the summer, maintaining indoor temperatures within the boundaries of comfort necessitates the incorporation of supplementary cooling equipment.

In the context of winter conditions, Figure 7c presents an analysis of temperature variances within a brick house. Notably, the outdoor temperature experiences pronounced fluctuations, exhibiting a peak difference of 5.6 °C. The average outdoor temperature is recorded at −4.1°C, with its lowest point at −5.1 °C observed around 7:00 and its highest point reaching 5.5 °C at 14:05. In contrast, the average indoor temperature during the winter season attains a maximum of 7.9 °C, peaking at 10.1 °C approximately at 14:50, and reaching its lowest point of 5.7 °C between 7:00 and 8:00, with a lag of approximately one hour compared to the outdoor minimum temperature.

Finally, Figure 7d portrays a gradual fluctuation in outdoor relative humidity during the winter period, oscillating within the range of 17% to 6%. This fluctuation can be attributed to the porous nature of rammed earth construction and its inherent capability to regulate humidity levels.

During the winter season, an examination of the temperature differential between indoor air and the inner surface of the structure reveals an average of −0.23 °C, with a range spanning from −1.8 °C to 0.4 °C. Although this variance is generally negligible for the majority of the time, a conspicuous upsurge in indoor air temperature is observed during the hours between 13:00 and 18:00. This temperature shift, reaching a peak difference of 1.8 °C from the inner surface temperature, raises concerns about the potential introduction of cold radiation, which has the potential to compromise indoor thermal comfort. It is worth noting that the inner surface temperature exhibits more subtle fluctuations when compared to the fluctuations observed in the outer surface temperature, emphasizing the superior thermal retention attributes associated with rammed earth construction. Lastly, as delineated in Figure 7e, the total annual solar radiation in the vicinity of Peking approximates 1500 kWh/m², with an annual average of 140 W/m². This ample solar energy resource presents a promising opportunity for reducing indoor energy consumption through the integration of solar technologies.

3.2. Simulation Result

To attain a comprehensive comprehension of indoor energy consumption, our study encompassed a year-long simulation, spanning a total of 8760 h. Figure 8a vividly illustrates the indoor temperature dynamics, fluctuating between 1.1 °C and 29.5 °C, with an annual mean of 15.9 °C. In sharp contrast, the outdoor temperature exhibits pronounced oscillations, ranging from −14.1 °C to 36.9 °C. Notably, the average outdoor temperature stands at 12.6 °C lower than its indoor counterpart.

The evaluation of thermal comfort is based on the Predicted Mean Vote (PMV), as prescribed by the Design Code for Heating, Ventilation, and Air Conditioning of Civil Buildings [37,38,39,40]. Ideally, the PMV should fall within the range of −0.5 and 0.5 for comfortable conditions. However, as depicted in Figure 8b, the room’s PMV varies between −2.7 and 1.79, indicating that for a substantial portion of the year, indoor thermal conditions deviate from the comfort range. Particularly during winter, indoor thermal comfort is found to be suboptimal, leading to thermal discomfort experienced for approximately 86% of the year. Given these compelling findings, it is imperative to explore key design elements to enhance the energy efficiency of brick houses.

3.3. Model Validation

As shown in Figure 9, we analyzed the error between the simulated and measured values, and the error analysis index in statistics was applied, namely, relative mean bias error (RMSErel), which is illustrated in Section 2.4.1 Equation (1), where Mi is considered the measured value (Y axis), Si is the simulation value from the model (X axis), and n is the sample size; this research chose simulated data generated on the same day as the actual measurements for comparison. According to the ASHRAE guidelines, a building model is calibrated if hourly RMSE values fall below 2%. By calculation, in this model, RMSE ≤ 1%. That means experimental results verify the validity of the model.

3.4. Sensitivity Analysis Result

3.4.1. Global Sensitivity Analysis Result

A sensitivity analysis was conducted using 200 samples, utilizing selected parameters within the SPSS-26.0 software. Two distinct metrics were employed: the standardized rank regression coefficient (SRRC) and the partial rank correlation coefficient (PRCC). Both metrics were calculated through regression methods and served as sensitivity indices. While SRRC assesses linear relationships among parameters, PRCC provides a sensitivity evaluation without incorporating the correlation effects among parameters [41].

Figure 8 illustrates the impact of SRRC and PRCC on the Predicted Mean Vote (PMV) for each parameter. The analysis highlighted that, among the nine chosen design factors, the thickness of roof insulation emerged as the most critical factor for achieving thermal comfort. Additionally, factors such as roof thickness, external wall insulation thickness, wall thickness, and the type of insulation used for roofs were identified as highly sensitive factors positively correlated with thermal comfort. Conversely, factors like glazing type and orientation exerted a negative effect, while the depth of the sunspace and the type of insulation used for external walls had minimal impact. It is noteworthy that PRCC values exceeded those of SRRC, indicating the presence of nonlinear effects.

The results from the sensitivity analysis emphasize the primacy of building insulation parameters over transparent parameters associated with the attached sunspace in ensuring thermal comfort. In pursuit of optimal comfort, both the thermal insulation of the building and its thermal storage capabilities should be primary considerations during the design phase. The PMV demonstrates improvement with a decrease in heat transfer coefficient, suggesting that the utilization of high-quality LOW-E glazing and low-emissivity windows can enhance thermal comfort, particularly during colder seasons.

Figure 10 and Figure 11 provide in-depth insights into the outcomes of the sensitivity analysis concerning energy consumption. Notably, roof thickness emerges as the most influential determinant of energy usage, with roof insulation thickness and external wall insulation thickness following closely as significant parameters affecting energy consumption. While sunspace depth has a negative impact on energy consumption, factors such as glazing window type, wall thickness, wall insulation thickness, and roof insulation thickness continue to dominate as key contributors. A noticeable nonlinear relationship is observed between the depth of the sunspace and energy consumption, indicating that as the sunspace extends beyond 1.5 m, total energy consumption increases. This phenomenon can be attributed to the expanded glazing area within the sunspace, which, while allowing for greater solar absorption, simultaneously leads to heat loss during colder conditions. The use of high-performing glazing and insulation materials can help mitigate this heat loss but may require a higher initial investment. Therefore, finding an equilibrium depth for the sunspace, and balancing heat gain and loss becomes crucial. Additionally, when designing buildings adjacent to sunspaces, the roof’s design plays an integral role in energy conservation.

Prior research has underscored the significance of the interplay between heat collection, storage, and insulation in passive sunspace design. This study complements and extends these insights. Components that store heat absorb energy during daylight hours, subsequently delaying temperature drops and mitigating temperature fluctuations. Effective insulation techniques can counteract heat loss from glazing windows, particularly those with high heat transfer coefficients, preserving the energy stored within materials and maximizing solar utility. It is crucial to recognize that the primary factor influencing the thermal performance of a building envelope is its insulation elements. In the absence of an internally located heat-storing roof, the wall assumes a pivotal role as both a primary insulator and thermal storage component in this context. Simultaneously, improving the quality of glass layers is indispensable for achieving optimal thermal comfort.

3.4.2. Single-Factor Sensitivity Analysis

Following the global sensitivity analysis discussed in Section 3.4.1, we conducted a local sensitivity analysis on parameters that demonstrated high sensitivity. The objective of this analysis was to determine the direction of influence of these parameters on both thermal comfort (indicated by PMV) and energy consumption. We also examined factors such as glazing type, wall thickness, insulation thickness, and the depth of the attached sunspace. Figure 12 provides visual representations of the results of the local sensitivity analysis, with the x-axis depicting varying levels of factors and the y-axis indicating the PMV value and annual energy consumption of the building.

As depicted in Figure 12a, the choice of roof glazing type has a profound impact on energy consumption. Specifically, selecting G4, as detailed in Table 3, leads to optimal energy savings, resulting in a yearly reduction of 12,215.15 kWh, which represents a decrease of 784.85 kW/h compared to other glazing types. Therefore, for cost-efficiency and maintaining consistent indoor PMV comfort throughout the year, G4 emerges as the preferred glazing type.

Figure 12b provides insights into the influence of roof insulation thickness on both energy consumption and comfort. It is evident that increasing insulation thickness consistently improves both aspects. Particularly, within the range of 0 mm to 15 mm, the impact of insulation thickness variation on energy conservation becomes more pronounced as the curve steepens. Beyond this range, while energy consumption continues to decrease, the reduction becomes gradual. Excessive increases in roof insulation thickness may result in higher costs without substantial energy-saving benefits. Therefore, the optimal insulation thickness range for the roof is found to be between 5 mm and 15 mm. Notably, as thickness escalates from 15 mm to 45 mm, comfort begins to deteriorate. Consequently, the 5 mm to 15 mm range is identified as the ideal design scope, yielding maximum annual energy savings of 10,205.42 kWh, which represents a reduction of 2514.58 kWh, and an increase in PMV from −0.62 to −0.43.

Figure 12c depicts the sensitivity analysis of sunspace depth. The relationship between sunspace depth and energy consumption appears to be nonlinear, with energy consumption spiking as sunspace depth exceeds 1.6 m. A deeper sunspace enhances the glazing area, allowing for more solar energy capture but also increasing the risk of heat loss through transparent sections during colder periods. While enhanced glazing and insulation materials can mitigate this heat loss, they also entail higher initial costs. An optimal sunspace depth of 1.6 m captures sufficient solar radiation in winter while avoiding the dominant wind direction. However, in the unique climatic and geographical context, harnessing solar radiation is of paramount importance, making southern orientation vital. An efficiency balance between comfort and energy consumption is achieved within depths of 1.4 m to 1.6 m, optimizing annual energy savings to 12,115.44 kWh, which is a reduction of 884.6 kWh.

In Figure 12d, we examine the influence of wall thickness on both energy consumption and comfort. It is evident that as wall thickness increases, both energy consumption and comfort improve simultaneously. Notably, within the range of 200 mm to 800 mm, the curve steepens, indicating that changes in wall thickness have a significant impact on energy savings. While continuous increases in thickness consistently lead to reduced energy consumption, the rate of reduction becomes less pronounced over time. Excessive augmentation of wall thickness may create a waste of resources and increase construction difficulties without proportionate energy savings. Consequently, the optimal design range for wall thickness is identified to be between 200 mm and 800 mm. It is important to note that while thermal comfort generally follows the trends observed in energy consumption, the comfort growth rate starts to decline as wall thickness extends from 800 mm to 1600 mm. Therefore, for achieving maximum energy efficiency, the best thickness is 800 mm, and annual savings can reach their peak at 12,255.32 kWh, representing a net reduction of 744.68 kWh.

Figure 12e provides insights into the effects of wall insulation thickness on both energy consumption and comfort. Similar to wall thickness, increasing insulation thickness enhances both energy consumption and comfort. Within the range of 0 mm to 50 mm, the curve exhibits a steep incline, indicating that variations in insulation thickness have a profound impact on energy conservation. Although energy consumption decreases with greater insulation thickness, this reduction becomes progressively less pronounced. Excessive increases in wall insulation thickness can result in waste without significant energy savings. Therefore, the ideal insulation thickness range for walls is identified to be between 20 mm and 50 mm. While thermal comfort generally aligns with the trends observed in energy consumption, it is important to note that comfort levels begin to diminish as insulation thickness expands from 60 mm to 200 mm. Consequently, optimizing insulation thickness within the range of 20 mm to 50 mm achieves the most efficient energy savings, approximately 10,915 kWh annually, representing a decrease of 1997 kWh.

The following indices were employed for the comprehensive assessment of the energy utilization of the building under investigation in this study. E_heating denotes the annual total energy consumption per unit building area, while E_CO2 quantifies the carbon dioxide (CO₂) emissions per unit building area, which is expressed in Equation (3) [3].

E_CO2 = E_fuel · C_con

(3)

Herein, ‘E_CO2’ signifies the carbon dioxide emissions per unit building area, measured in kilograms (kg). ‘E_fuel’ corresponds to the total fuel consumption per unit building area, which is derived from the heating or cooling system and calculated using simulation software, and is expressed in kW·h. Additionally, ‘C_con’ designates the carbon dioxide conversion factor, representing the quantity of carbon dioxide emitted per unit of energy consumption.

The Chinese Ministry of Housing and Urban-Rural Development has divided China into seven different regions, with Peking falling under the Northern Regional category, characterized by an emission factor of 0.55 [42]. Conducting a heat balance analysis of a building proves beneficial in elucidating the dynamics of heating energy consumption [22]. Hence, a statistical analysis of heat transfer through the building envelopes was performed to assess the heat flux emanating from the buildings. These aforementioned indices, in conjunction with CO₂ emissions, are pertinent to the evaluation of the energy consumption efficiency of individual buildings.

As depicted in Figure 13, upon the selection of the most suitable update strategy in Section 3.4.1, the daily carbon dioxide (CO₂) emissions originating from the roof exhibited a range of 5 to 25 kg on 49% of the annual calendar days. In contrast, following the retrofit, the roof’s daily CO₂ emissions fell within the range of 0 to 5 kg for 51% of the year. The wall, pre-retrofit, contributed to daily CO₂ emissions ranging from 5 to 45 kg for 59% of the annual duration, while post-retrofit observations showed that daily CO₂ emissions in this category decreased to 5–25 kg for 50% of the year. Before the retrofit, the glazing component was responsible for daily CO₂ emissions within the 0 to 5 kg range for approximately 73% of the annual timeframe. However, post-retrofit, this specific range of CO₂ emissions only accounted for 11% of the yearly duration. This noteworthy shift in emissions can be attributed to the enhanced thermal insulation of the building envelope, resulting in significantly reduced energy consumption throughout the remaining aspects of the interior construction and air infiltration. Total annual CO₂ emissions have been reduced by 3799.79 kg.

3.4.3. Annual Solar Tile Roof Power Generation

Figure 14a illustrates the performance of material P1, as outlined in Table 4 when utilized as a medium for renewing solar tile roofing. This material yields an annual electricity generation of 7659 kWh and demonstrates a solar radiation conversion efficiency of 10%.

In Figure 14b, we observe the results of employing material P2, also referenced in Table 4, to renew solar tile roofing. Material P2 achieves an annual electricity output of 12,732 kWh with a remarkable conversion efficiency of 17%.

Moving on to Figure 14c, we investigate the utilization of material P3, listed in Table 4, for the renewal of solar tile roofing. This material leads to electricity production of 10,075 kWh annually, boasting a solar radiation conversion efficiency of 13%.

Lastly, in Figure 14d, we explore the performance of material P4, as detailed in Table 4, when employed for the renewal of solar tile roofing. Material P4 results in an annual electricity generation of 12,057 kWh, accompanied by a solar radiation conversion efficiency of 16%.

3.4.4. PV System Economic Impact Financial

In situations involving household decision-making, financial considerations invariably take precedence, particularly when there is a need to determine the pay-back period (PBP) for an investment. This entails identifying the point at which electricity generation becomes cost-effective. For residents, it is of paramount interest to ascertain whether the installation of a photovoltaic (PV) system on tile roofing represents a sound financial investment, particularly given the potentially high initial capital outlay associated with PV panels. At present, local electricity prices stand at approximately CNY 0.588 per kilowatt-hour (kWh) at 2023 rates, with a warranty period of 25 years; the equipment incurs a modest maintenance fee over 25 years, and yet, its operational lifespan can surpass 35 years.

One critical aspect to consider is the ability to achieve equilibrium in return on investment (ROI). This equilibrium hinges on various factors, including the time value of money and the selection of nominal and real discount rates when conducting a whole-life cost appraisal (WLC).

To evaluate the economic viability of such projects, two key financial metrics, namely the pay-back period (PBP) and the net present value (NPV), were employed. These analyses serve as instrumental tools for assessing the economic impact and feasibility of implementing PV systems on tile roofing in a residential setting.

Table 6. NPV analysis calculations for installing a PV system on the brick dwelling.

ASE 300-DFG/50			BP Solar 380		Shell s115		Uni-Solar PVL-128
Total Investment = CNY 69,010			Total Investment = CNY 111,220		Total Investment = CNY 85,425		Total Investment = CNY 101,840
Unit cost = 1030 CNY/m2			Unit cost = 1560 CNY/m2		Unit cost = 1275 CNY/m2		Unit cost = 1420 CNY/m2
annual profit = CNY 4504			annual profit = CNY 7486		annual profit = CNY 5924		annual profit = CNY 7090
Inflation rate, d = 2.5%			Inflation rate, d = 2.5%		Inflation rate, d = 2.5%		Inflation rate, d = 2.5%
Years	Cash flow (CNY)	NPV (CNY)	Cash flow (CNY)	NPV (CNY)	Cash flow (CNY)	NPV (CNY)	Cash flow (CNY)	NPV (CNY)
1	4394.1	−64,615.9	7303	−103,917	5779.5	−79,645.5	6917.1	−94,922.9
2	4285.5	−60,330.4	7123.2	−96,793.8	5636.5	−74,009	6745.9	−88,177
3	4185.9	−56,144.5	6957.2	−89,836.6	5505.6	−68,503.4	6589.2	−81,587.8
4	4079.7	−52,064.8	6780.8	−83,055.8	5366	−63,137.4	6422.2	−75,165.6
5	3982.3	−48,082.5	6618.9	−76,436.9	5237.8	−57,899.6	6268.7	−68,896.9
6	3882.7	−44,199.8	6453.5	−69,983.4	5106.9	−52,792.7	6112.1	−62,784.8
7	3788.1	−40,411.7	6296	−63,687.4	4982.3	−47,810.4	5963	−56,821.8
8	3697.9	−36,713.8	6146.2	−57,541.2	4863.8	−42,946.6	5821	−51,000.8
9	3608.9	−33,104.9	5998.4	−51,542.8	4746.8	−38,199.8	5681.1	−45,319.7
10	3518.8	−29,586.1	5848.4	−45,694.4	4628.1	−33,571.7	5539.1	−39,780.6
11	3432.9	−26,153.2	5705.8	−39,988.6	4515.2	−29,056.5	5403.9	−34,376.7
12	3351.2	−22,802	5569.9	−34,418.7	4407.8	−24,648.7	5275.3	−29,101.4
13	3266.1	−19,535.9	5428.6	−28,990.1	4295.8	−20,352.9	5141.4	−23,960
14	3189.8	−16,346.1	5301.7	−23,688.4	4195.5	−16,157.4	5021.3	−18,938.7
15	3110.5	−13,235.6	5169.9	−18,518.5	4091.2	−12,066.2	4896.4	−14,042.3
16	3034.7	−10,200.9	5043.8	−13,474.7	3991.3	−8074.87	4776.98	−9265.32
17	2960.5	−7240.31	4920.76	−8553.94	3894.02	−4180.85	4660.47	−4604.85
18	2888.41	−4351.9	4800.75	−3753.19	3799.05	−381.799	4546.8	−58.04
19	2817.96	−1533.94	4683.66	930.47	3706.39	3324.591	4435.9	4377.859
20	2749.2	1215.282	4569.43	5499.9	3615.99	6940.58	4327.71	8705.571
21	2682.2	3897.454	4457.98	9957.887	3527.79	10,468.37	4222.16	12,927.73
22	2616.8	6514.208	4349.25	14,307.14	3441.76	13,910.13	4119.18	17,046.91
23	2552.9	9067.138	4243.16	18,550.3	3357.8	17,267.93	4018.71	21,065.62
24	2490.7	11,557.8	4139.68	22,689.98	3275.91	20,543.84	3920.69	24,986.31
25	2429.9	13,987.72	4038.71	26,728.69	3196.01	23,739.85	3825.07	28,811.38

provides information on four distinct PV panels, including details on the approximate initial system cost, which encompasses the total investment price, cost per unit of square footage, economic benefits generated during the year of electricity production, the inflation rate, and the annual return on investment amount. For the simulated residential house with a roof area of 66 square meters, the total investment of the ASE 300 system is CNY 69,010, and since the price of electricity in Peking is CNY 0.588, the annual value of solar power generation is CNY 4504, the total investment of the BP 300 system is CNY 111,220, the annual value of solar power generation is CNY 7486, the total investment of the Shell s115 300 system is CNY 85,425, the annual value of solar power generation is CNY 5924, the total investment of the Uni 128 system is CNY 101,840, and the annual value of solar power generation is CNY 7090.

According to Figure 15, the installation of the Uni-Solar PVL-128 system is poised to be a highly favorable investment. It is expected to generate revenue close to CNY 101,840 (equivalent to USD 14,144). The system’s annual electricity production is estimated at around 12,057 kWh, and it is anticipated to reach the break-even point in the 18th year, considering the residential electricity tariff of CNY 0.588. Furthermore, over its 25-year service life, the system is projected to yield a profit of CNY 28,811.38.

A summary of the monthly produced energy records is presented in Figure 16. The simulation shows that monthly power generation exceeds 600 kWh. Based on Electricity and Water Authority (EWA) data, each house consumes approximately 450 kWh per month, therefore, installing a PV system on the roof can meet all the needs of residential electricity consumption, and the excess electricity can be integrated into the grid to supply industrial electricity. According to the CO₂ conversion rate formula discussed in Section 3.4.3, the approximate annual reduction in CO₂ emissions after installation of a roof PV system is approximately 6631.35 kg.

4. Conclusions

This study employed a combination of experimental and numerical approaches to investigate mitigating energy consumption and reducing CO₂ emissions of a brick dwelling located in the Peking area. The following conclusions can be drawn from the findings of this research:

(1)

Field experiments have revealed that the average annual solar radiation in the area amounts to 140 W/m². The outdoor temperature experiences significant fluctuations during both the winter and summer seasons. Despite some inherent thermal insulation properties of local brick dwellings, they have not been able to provide a consistently comfortable indoor living environment. Consequently, measures such as cooling and heating are necessary to enhance indoor thermal comfort.

(2)

The simulation results have shown that the annual reduction in energy consumption for rooms with attached sunspaces maximum value is 884.6 kWh. Notably, there exists a nonlinear correlation between the depth of the sunspace and energy consumption, indicating that total energy consumption increases as the depth of the sunspace exceeds 1.6 m.

(3)

At a wall thickness of 800 mm, there is a reduction of 744.68 kWh in indoor energy consumption. Similarly, with the preferred thickness of roof insulation material set at 15 mm, indoor energy consumption decreases by 2514.58 kWh. Additionally, a recommended outer wall insulation material thickness of 50 mm results in a decrease of 1997 kWh in indoor energy consumption. The implementation of a well-suited brick dwelling renewal design has led to a reduction in the annual CO₂ emissions of a single residence by approximately 3799.79 kg.

(4)

When employing Photovoltaic tiles of type BPsolar 380, the annual electricity production is estimated to reach 12,057 kWh, and the approximate annual reduction in CO₂ emissions after installation of a roof PV system is approximately 6631.35 kg.

This study serves as a valuable resource for architects seeking guidance on the control of energy consumption and CO₂ emissions within brick dwellings situated in the Peking area. Expanding our understanding of indoor thermal environments across various residential building types. However, it is important to emphasize that within this paper, the examination of parameters’ impact on thermal comfort and energy consumption has been conducted separately. It is important to acknowledge that certain factors may have contrasting effects on thermal comfort and energy consumption. Subsequent endeavors will encompass extensive optimization initiatives directed toward the minimization of performance objectives, with the ultimate aim of attaining a globally optimal design that strikes a harmonious balance between economic efficiency and energy efficiency.