Review and Decision-Making Tree for Methods to Balance Indoor Environmental Comfort and Energy Conservation During Building Operation

Abstract

Effective building operation requires a careful balance between energy conservation and indoor environmental comfort. Although numerous methods have been developed to reduce energy consumption during the operational phase, their objectives and applications vary widely. However, the complexity of building energy management makes it challenging to identify the most suitable methods that simultaneously achieve both comfort and efficiency goals. Existing studies often lack a systematic framework that supports integrated decision-making under comfort constraints. This research aims to address this gap by proposing a decision-making tree for selecting energy conservation methods during building operation with an explicit consideration of indoor environmental comfort. A comprehensive literature review is conducted to identify four main energy-consuming components during building operation: the building envelope, HVAC systems, lighting systems, and plug loads and appliances. Three key comfort indicators—thermal comfort, lighting comfort, and air quality comfort—are defined, and energy conservation methods are categorized into three strategic groups: passive strategies, control optimization strategies, and behavioural intervention strategies. Each method is assessed using a defined set of evaluation criteria. Subsequently, a questionnaire survey is administered for the calibration of the decision tree, incorporating stakeholder preferences and expert judgement. The findings contribute to the advancement of understanding regarding the co-optimization of energy conservation and occupant comfort in building operations.

1. Introduction

As climate change issues become increasingly prominent and the concept of sustainable development becomes more popular, building energy conservation has become a global consensus [1]. According to the report from International Energy Agency in 2022, buildings account for approximately 30% of the world’s final energy consumption and 26% of energy-related emissions. Of these emissions, 8% are direct emissions from buildings, while 18% are indirect emissions resulting from the generation of electricity and heat for buildings [2]. Nineteen cities worldwide have declared that all new buildings and 20% of existing building stock need to be zero-carbon ready by 2030, with the goal of achieving net-zero for all buildings by 2050 [3]. However, buildings are not only energy-consuming entities but also environments where people live and work [4]. A considerable amount of energy is used to maintain indoor environmental comfort, particularly for heating, ventilation, and air conditioning (HVAC) systems to ensure thermal comfort [5]. In U.S. commercial buildings, HVAC systems alone account for approximately 50% of total energy consumption [6]. The building operation phase is a critical period for balancing energy conservation and indoor environmental comfort [7].

Optimizing energy efficiency while considering indoor environmental comfort during the building operation phase has become a central focus [8]. Many studies have explored methods to evaluate and optimize the balance between indoor environmental comfort and energy conservation. Raw et al. [9] carried out detailed surveys of more than 2000 UK households to link energy consumption and indoor environmental quality, promoting a balance between energy conservation and indoor environmental comfort. Ribeiro et al. [10] performed a literature review on energy efficiency and comfort in various indoor spaces, highlighting the influence of transitional areas within buildings. Yuan et al. [11] demonstrated that the ventilation system plays a crucial role in maintaining thermal comfort for patients and stuff in hospital buildings.

Niza et al. [12] demonstrated that thermal comfort, lighting comfort, air quality comfort and sound comfort are measurable factors that affect building energy consumption during the building operation phase. Amasyali and El-Gohary [13] examined how different energy usage modes impact building energy efficiency while maintaining thermal comfort. Gatea et al. [14] evaluated the relationship between energy management strategies and thermal comfort in hospital buildings. Matsui and Saito [15] proposed a networked lighting system to improve indoor light comfort, validated through long-term field data. Park and Nagy [16] developed reinforcement learning-based lighting controllers to balance occupant comfort with energy efficiency. Zhang et al. [17] introduced a demand-controlled ventilation (DCV) approach using CO₂ concentration to ensure air quality while reducing unnecessary mechanical ventilation. Yu et al. [18] evaluated the performance of integrated air-handling unit (IAHU) control to coordinate indoor air quality and energy efficiency in open-plan offices. Despite the substantial theoretical and practical contributions from prior studies, existing research remains limited in two key areas: first, many studies focus on isolated aspects without offering a comprehensive systematic analysis, and second, practical tools to support decision-making are often lacking in technical method selection.

The objective of this paper is to conduct a comprehensive literature review of energy conservation methods applicable during building operation, with a particular emphasis on strategies that account for indoor environmental comfort. This paper is structured as follows: Section 2 introduces the significance of balancing energy efficiency and occupant comfort in building operation and outlines the design of the literature review. Section 3 identifies the main energy-consuming components and establishes the key comfort indicators. Section 4 systematically reviews existing energy conservation methods, categorized into passive design strategies, control optimization strategies, and behavioural intervention strategies, highlighting their respective impacts on thermal, visual, and air quality comfort. Section 5 synthesizes the findings and presents a decision-making tree, developed based on questionnaire results, to guide the practical selection of comfort-aware energy conservation methods. Finally, Section 6 concludes the study by summarizing key insights and proposing directions for future research.

2. Research Methodology

2.1. Significance of Energy Conservation and Indoor Environmental Comfort During Building Operation

The operational phase of buildings is generally the most energy-intensive stage in their life-cycle and thus plays a critical role in optimizing energy consumption [19]. Research indicates that the energy required for building operation—including HVAC systems, lighting, and plug loads—typically accounts for 80–90% of a building’s total life-cycle energy consumption. Therefore, operational-phase measures have the greatest potential for reducing overall energy use and associated carbon emissions [20]. Effective energy management not only reduces operating costs and environmental impact but also prolongs equipment lifespan by minimizing operational stress [21].

Simultaneously, maintaining indoor environmental quality (IEQ) remains critical to occupant well-being. The provision of thermal comfort, adequate lighting, and good air quality is essential for safeguarding health and sustaining work performance [22]. It has been perceived that energy efficiency methods may conflict with occupant comfort. For example, an excessive reduction in HVAC or lighting usage often leads to a compromised occupant experience. However, recent research and practice in sustainable building design indicate that energy conservation and occupant comfort are not inherently contradictory; in many cases, they can result in mutually beneficial scenarios [23]. For instance, improving insulation or optimizing HVAC control can stabilize indoor temperatures and enhance thermal comfort, while the strategic use of natural light can improve visual comfort. These methods not only reduce energy consumption but also maintain or improve occupant comfort. Therefore, achieving energy conservation while ensuring comfort is particularly important during building operation, as discomfort may reduce user satisfaction and productivity, thereby undermining the fundamental goals of sustainability.

2.2. Literature Review Design

Given the imperative to simultaneously enhance building energy efficiency and maintain indoor environmental comfort, a holistic and structured approach is adopted to examine energy conservation methods implemented during building operation. The primary objective is to identify strategies that reduce energy consumption while ensuring that key comfort indicators are not compromised. The review is structured in three phases.

First, the main components of energy consumption are identified in the context of building operation. Simultaneously, key indoor environmental comfort indicators are also summarized and used as essential constraints for evaluating the feasibility of energy conservation methods. Next, a categorization framework is developed to classify the reviewed energy conservation methods into three major groups: passive design strategies, control optimization strategies, and behavioural intervention strategies. Each method is analysed in terms of its operational mechanism (building type, regions and climates/seasons), its contribution to the improvement of energy performance (algorithm and tools), and its impact on comfort dimensions (targets and objects/index). Emphasis is placed on identifying the extent to which energy conservation can be achieved without compromising comfort outcomes.

The literature search was conducted in accordance with systematic review standards. Peer-reviewed articles were retrieved from academic databases such as the Web of Science, Google Scholar, and ScienceDirect, as well as other recognized scientific publishing platforms. To ensure comprehensiveness and credibility, a structured search strategy was employed, incorporating a combination of keywords related to “building energy conservation”, “HVAC control”, “thermal comfort”, “indoor air quality”, and “smart building operation”, with detailed keyword categories listed in

Table 1. The summarized keywords for the systematic literature review.

Category	Keywords
Passive strategies	TS = ((“energy efficiency” OR “energy conservation” OR “energy saving”) AND (building OR buildings) AND (“passive design” OR “passive strategy” OR “passive strategies” OR “passive cooling” OR “passive heating” OR “natural ventilation” OR “passive lighting”) AND (“daylighting” OR “building envelope” OR “insulation” OR “shading”) AND (“method” OR “case study” OR “simulation” OR “field experiment” OR “technology”) AND (“indoor environment” OR “indoor environmental quality” OR “occupant comfort” OR “thermal comfort” OR “lighting comfort” OR “indoor air quality”))
Operation control optimization strategies	TS = ((“energy efficiency” OR “energy management” OR “energy conservation”) AND (“operational phase” OR “building operation”) AND (“control” OR “smart control” OR “intelligent control” OR “adaptive control” OR “predictive control” OR “automated control” OR “model predictive control” OR “building automation” OR “control system” OR “monitoring system” OR “fault detection” OR “smart building” OR “building management system” OR “BEMS” OR “energy monitoring” OR “detection” OR “prediction” OR “optimization”) AND (building OR buildings) AND (“method” OR “case study” OR “simulation” OR “experiment” OR “field study” OR “approach” OR “framework” OR “technology” OR “application”) AND (“indoor environment” OR “indoor environmental quality” OR “occupant comfort” OR “thermal comfort” OR “lighting comfort” OR “in-door air quality”))
Behavioural intervention strategies	TS = ((“energy efficiency” OR “energy management” OR “energy conservation”) AND (building OR buildings) AND (“occupant behaviour” OR “user behaviour” OR “behavioural change” OR “human factors” OR “occupant interaction” OR “occupancy pat-terns” OR “energy awareness” OR “energy feedback” OR “operational policy” OR “education” OR “incentive”) AND (“method” OR “case study” OR “simulation” OR “field experiment” OR “technology”) AND (“indoor environment” OR “indoor environmental quality” OR “occupant comfort” OR “thermal comfort” OR “lighting comfort” OR “in-door air quality” OR “IEQ”))

.

Articles were included if they were published in English between 2019 and 2024, focused on the operational phase of buildings, addressed both energy conservation performance and indoor environmental comfort-related outcomes, and presented empirical findings or simulation-based validation. Studies were excluded if they solely discussed theoretical models without practical application, addressed either comfort or energy in isolation, or focused exclusively on architectural design without operational relevance. The screening process involved establishing an initial pool of publications, removing duplicates, conducting title and abstract screening, and assessing full-text eligibility. Only studies that met all inclusion criteria were retained for in-depth analysis.

3. Main Components and Key Comfort Indicators

3.1. Main Energy-Consuming Components During Building Operation

3.1.1. Building Envelope

A building envelope refers to the energy-impacting components enclosing heated and cooled spaces within a building. The envelope system can be categorized into internal and external components. While internal envelope elements serve primarily to partition functional spaces, the external envelope acts as a thermal interface between indoor and outdoor environments, significantly influencing indoor thermal conditions, heating and cooling supply, ventilation, and air conditioning systems [24]. The building envelope can be further classified into opaque and transparent components [25]. Opaque elements comprise roofs, exterior walls, ground floors, and cantilever slabs, whereas transparent elements typically consist of external windows and glass curtain walls.

The energy efficiency of building envelopes is predominantly determined by their orientation, configuration, and the optical and thermophysical properties of exterior walls and other envelope components [26]. An efficiently designed building envelope can substantially reduce the demand for heating and cooling systems [27].

3.1.2. HVAC Systems

HVAC systems typically consume the largest share of building energy; in U.S. commercial buildings, space heating, ventilation, cooling and water heating account for 32%, 11%, 9% and 5% of total energy use, respectively [6]. An inverse relationship exists between building efficiency and HVAC system requirements [25]. Well-designed and intelligent buildings can minimize or eliminate heating and cooling demands while reducing ventilation requirements.

Building heating can be achieved through various systems, primarily categorized into centralized and individual heating methods. Centralized heating systems may incorporate building-based combination systems, such as boilers, or external supply sources like district heating and combined heat and power systems. Buildings can also derive heat from standalone systems, including electric heaters, heat pumps, or individual furnaces. Heating functionality may be integrated into ventilation and air conditioning systems, encompassing distribution components such as pipes, ducts, storage tanks, pumps, fans, and heat exchangers [28].

Cooling systems can be implemented either centrally or through distributed units installed in individual rooms, such as small split-unit systems. In split-unit installations, the efficiency of cooling equipment and control systems is crucial for overall performance. For centralized systems, system sizing, controls, and distribution ductwork collectively determine energy efficiency. Building airtightness is particularly critical for cooling applications, as air leakage can significantly compromise mechanical cooling efficiency. Some buildings utilize natural cooling or night cooling strategies, both of which reduce the demand for active cooling systems [25].

Ventilation systems are classified into passive (natural) and active (mechanical) ventilation. Well-insulated, airtight buildings typically require active ventilation to ensure fresh air exchange for occupants. Both natural ventilation, such as airflow through open windows, and mechanical ventilation facilitate air circulation [29]. Ventilation capabilities can be integrated into air conditioning systems that provide both heating and cooling functions. Various technologies, including heat exchangers and heat pumps, can enhance ventilation system efficiency. However, a critical consideration is the heat loss associated with air exchange [30].

3.1.3. Lighting Systems

Lighting systems represents the third largest source of energy consumption after HVAC systems, with both direct energy demands and waste heat generation contributing to increased loads [31]. In commercial buildings, lighting systems account for approximately 10% of total energy consumption [6]. Consequently, reducing lighting loads can significantly decrease overall building energy consumption. International and regional organizations are developing more specific standards and regulations for lighting system energy efficiency measures [32]. Lighting system requirements, particularly during daylight hours, are determined by window dimensions, placement, and building conditions. Effective design strategies implemented during the building design phase can optimize lighting efficiency, resulting in substantial energy and cost savings [33].

During the operational phase of buildings, active control strategies can be implemented to reduce lighting demands based on window orientation, daylight availability, and room occupancy patterns [34]. Research has demonstrated that control schemes can effectively reduce lighting system energy consumption by automatically maintaining minimum electric lighting levels while accounting for natural light availability [35]. Additionally, indoor lighting systems generate heat, varying with installation type, which can serve as waste energy. This thermal output can decrease heating energy demands in cold climates or winter seasons while potentially increasing cooling requirements in hot climates or summer months [36].

3.1.4. Plug Loads and Appliances

In addition to HVAC and lighting systems, many office devices, household appliances, and other plug-in equipment—such as computers, printers, refrigerators, and televisions—are commonly found in buildings and are collectively referred to as “socket loads” or “white appliances.” These devices not only consume electricity during daily operation but also generate internal heat gains, thereby influencing indoor temperature and increasing HVAC demand [37]. As other systems become more efficient, the proportion of total energy consumption attributable to socket loads has increased, drawing increasing attention in energy conservation research. Without compromising user productivity or convenience, energy consumption can be significantly reduced through the adoption of energy-efficient devices and the implementation of appropriate control strategies [38].

3.2. Key Indicators of Indoor Environmental Comfort

To incorporate comfort into a decision-making tree for energy conservation, it is first necessary to understand how comfort is measured. Focus is placed on three indicators, thermal comfort, lighting comfort, and indoor air quality comfort, each of which is associated with an established evaluation metric.

3.2.1. Thermal Comfort Indicators

The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) defines thermal comfort as a psychological state of satisfaction with the thermal environment [39]. Similarly, the International Organization for Standardization (ISO) characterizes thermal comfort as the subjective satisfaction evaluation of the surrounding thermal environment. Multiple factors influence thermal comfort, including environmental parameters (air temperature, mean radiant temperature, air velocity, and relative humidity) and human factors (metabolic rate and clothing thermal resistance), which collectively affect human thermal comfort perception [40].

Research into and the understanding of thermal comfort evaluation models have evolved continuously. In the 1920s, European scholars began investigating indoor thermal environments and comfort evaluation methods. Initial evaluation criteria considered only indoor temperature and relative humidity, later incorporating air velocity as research progressed. However, these early standards did not comprehensively address other thermal comfort factors, such as mean radiant temperature and human metabolic rate. In 1970, Danish Professor Fanger introduced the Predicted Mean Vote (PMV) index model, representing the most comprehensive thermal comfort evaluation metric at that time [41]. In 1984, the International Organization for Standardization established ISO7730 [42], a new standardized method for indoor thermal environment evaluation utilizing the PMV-PPD (Predicted Mean Vote–Predicted Percentage Dissatisfied) indices for evaluating thermal environments and human thermal comfort. This standard recommends maintaining PMV at level 0 with a tolerance of 0.5 for optimal thermal comfort [43]. The PMV-PPD approach remains the most widely accepted evaluation method. ANSI/ASHRAE Standard 55-2017 on thermal environmental conditions for human occupancy specifies compliance with either the PMV model or the adaptive model [44].

These indicators can be used to quantitatively assess the extent to which energy conservation methods meet thermal comfort requirements. For example, increasing the air conditioning setting by 1–2 °C can significantly save energy, but the PMV must still be within an acceptable range.

3.2.2. Lighting Comfort Indicators

Although individual perceptions and acceptance of visual comfort vary, making its evaluation somewhat subjective, various countries, regions, and researchers have established specific indicators and parameters to guide and evaluate lighting environment design based on practical conditions. Visual comfort encompasses subjective experiences related to illumination intensity, brightness, uniformity, and glare. Achieving comfortable visual effects in buildings can enhance occupant well-being [45]. Conversely, insufficient or excessive illumination, uneven light distribution, or improper spectral adjustment can lead to visual fatigue, discomfort, and potential vision impairment due to the eye’s inability to adapt [46].

ISO 8995-1:2002 [47], established by the International Organization for Standardization, specifies lighting requirements for indoor workplaces and criteria for efficient, comfortable, and safe visual task performance throughout the work period, primarily addressing illuminance (Ep), uniformity (Uo), and unified glare rating (UGR) [48]. The European Committee for Standardization’s EN 12464-1:2021 stipulates quantitative and qualitative requirements for lighting solutions in most indoor workplaces and associated areas. Additionally, it provides recommendations for good lighting practices, encompassing both visual and non-visual (non-image-forming) lighting needs, specifically addressing illuminance, uniformity, colour rendering index (CRI), and unified glare rating [49].

ASHRAE 90.1 [50] provides detailed minimum energy efficiency requirements for the design and construction of new sites, buildings and their systems, new portions of buildings and their systems, and new systems and equipment in existing buildings, excluding low-rise residential buildings, along with criteria for determining compliance [50]. The LEED green building rating system includes energy efficiency requirements and design guidelines for building lighting systems. The Energy and Atmosphere (EA) and Indoor Environmental Quality (IEQ) sections of the LEED system address lighting system energy efficiency and visual comfort requirements [51].

Key indicators such as illuminance, uniformity, colour rendering, and glare value provide objective measures for assessing a building’s visual comfort. Simultaneously, occupants’ subjective evaluations and psychological responses serve as crucial indicators of lighting environment quality, offering the most direct source of information for measuring visual comfort.

Given the multidimensional nature of visual comfort, comprehensive evaluation methods are typically employed to integrate these various indicators.

3.2.3. Indoor Air Quality Comfort Indicators

Understanding and controlling common indoor pollutants can help reduce the risk of indoor health issues. Good indoor air quality not only enhances occupant comfort but also directly impacts their health and productivity [52].

Research indicates that indoor air quality can be measured through various parameters including carbon dioxide (CO₂) concentration, particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), and carbon monoxide (CO). CO₂ levels are influenced by occupant activities and other pollution sources, such as nitrogen oxides, VOCs, and inhalable particles [53]. Additionally, appropriate air temperature and humidity significantly affect human thermal comfort. Therefore, effective on-site monitoring, ventilation evaluation, and user feedback are crucial methods for controlling indoor air quality [53]. Cociorva and Iftene conducted a qualitative evaluation of indoor air quality using an electronic nose to intelligently control heating, ventilation, and air conditioning systems, aiming to develop a unified control method that ensures air quality improvement while maintaining low energy consumption [54]. Zhu and Li proposed a grey clustering model for indoor air pollutants, utilizing grey absolute correlation degree to express the relationships between different indoor air pollutants in terms of their resources and propagation characteristics, thereby simplifying indoor air quality evaluation through pollution clustering based on the grey correlation matrix [55].

Indoor air quality comfort evaluation standards aim to ensure indoor environments promote occupant health and comfort. ASHRAE Standard 62.1 [56] on ventilation and acceptable indoor air quality specifies minimum ventilation rates and other measures designed to provide acceptable indoor air quality while minimizing adverse health effects. This standard provides procedures and methods for engineers, design professionals, building owners, and jurisdictional authorities to meet minimum ventilation and indoor air quality requirements [57]. ISO 16000 [58] establishes standards for indoor air quality through a series of specifications covering methods for sampling, measuring, and evaluating indoor air pollutants. The standard outlines various methods for measuring parameters such as temperature, humidity, and concentrations of various compounds, including volatile organic compounds (VOCs), formaldehyde, and particulate matter.

4. Energy Conservation Methods Considering Indoor Environmental Comfort

During the operational phase of buildings, a variety of energy conservation methods are available. However, the pursuit of energy efficiency alone may result in reduced comfort levels, whereas an exclusive emphasis on comfort can lead to increased energy consumption. Accordingly, the methods considered are based on the simultaneous maintenance or enhancement of indoor environmental comfort.

Although previous research has categorized such methods according to specific building systems [59], a more abstract, operation-phase framework is proposed to minimize overlap and improve conceptual clarity. All methods included can be implemented during the operational phase of a building, excluding major structural retrofits, and are explicitly designed to reduce energy consumption while preserving occupant comfort. The framework classifies these methods into three categories: (1) passive strategies, (2) control optimization strategies, and (3) behavioural intervention strategies. Each category is defined with representative literature examples, estimated energy conservation potential, and effects on indoor environmental comfort.

4.1. Passive Strategies

The objective of passive strategies in buildings is to optimize the utilization of natural resources and the building’s design features to satisfy the requirements for heating, cooling and lighting with minimal active energy use. These include methods like natural ventilation, daylight utilization, solar shading, and thermal mass storage [37]. Passive strategies are decided in the design phase (building orientation, envelope insulation, window design, etc.) [60]. In addition to retrofitting existing structures, numerous passive features are static in nature and cannot be adjusted dynamically beyond the original design [61].

However, it should be noted that passive strategies have the capacity to constitute a separate category during the operational phase. Indeed, some passive technologies can be actively managed during operation; for instance, windows can be opened to gain a cooling breeze, or blinds can be adjusted for sun shading, and windows can also be opened to increase lighting using natural light. Consequently, this paper retains passive strategies as a discrete category, given that they frequently function in conjunction with control optimization or occupant behaviour. The emphasis of passive strategies is on the exploitation of the inherent energy conservation potential of a building during daily operations.

To provide a structured overview of practical passive energy conservation strategies,

Table 2. Summary of passive strategies.

Year	Building Type	Regions	Climates/Seasons	Targets	Objects/ Index	Algorithm/ Approach	Tools	Reference
2021	Office buildings	Spain, Vietnam, Germany	Mediterranean, subtropical and moderate climate zone	Ventilation and indoor air quality	Louvre windows	Energy simulation	EnergyPlus 8.9.0	[62]
2021	Office buildings	China	Five representative climates range from subtropical to frigid regions	Ventilation	Exterior windows	Energy simulation	EnergyPlus 9.5.0	[63]
2022	Office buildings	China	Continental climates (non-heating season)	Cross-ventilation	Windows, doors	Thermal simulation	DesignBuilder v7	[64]
2022	Office buildings	Korea	Mixed-humid climate	Hybrid ventilation and temperature	Whole room	Thermal simulation	EnergyPlus 9.6.0	[65]
2022	Residential Buildings	China	Hot summer and cold winter	Thermal comfort	Window openable area and shading	Cooling energy consumption and thermal simulation	DesignBuilder v6	[66]
2023	School buildings	China	Cold and severe cold	Daylighting and thermal comfort	External shading	Numerical analyses	Ladybug Tools 1.6.0	[67]
2024	School buildings	Spain	Warm semi-arid dry Mediterranean climate	Thermal and lighting comfort	Sun shading devices	On-site measurements, user surveys and computer simulations	DesignBuilder v7	[68]
2025	Historic buildings	USA	Hot–humid climates	Natural ventilation	Whole building	CFD simulations	IES VE 2025	[69]

categorizes relevant methods based on their functional focus and implementation characteristics. Passive strategies are particularly effective in reducing energy loads without requiring continuous energy input or advanced control systems. These measures operate primarily through physical characteristics and environmental interactions, such as insulation, shading, or natural ventilation, rather than through mechanical or digital controls.

4.2. Control Optimization Strategies

Operation control optimization refers to strategies that save energy by improving the control logic, algorithms, or software that operate building systems [70]. These methods adjust how equipment runs in real time to eliminate inefficiencies. Crucially, they do not require major new hardware or physical retrofits—instead, they involve the smarter use of existing systems (often via building automation systems or control software) [19]. This category excludes purely human-driven measures or equipment improvement methods focusing on automated control improvements like advanced control algorithms [71].

Conventional feedback controllers, especially proportional–integral–derivative (PID) loops, remain ubiquitous in HVAC applications due to their simplicity and reliable basic performance [72]. These controllers can optimize system control performance within certain parameters to achieve rapid response, reduce steady-state errors, and enhance system stability. However, PID control, despite its implementation across diverse applications and challenging environmental conditions, consistently demonstrates performance below the optimal control solution benchmark [73]. Consequently, as the primary objective has shifted toward providing comfortable and healthy indoor environments for occupants, building environmental regulation methods have evolved from PID control approaches to more sophisticated, targeted, and intelligent control strategies.

Research into and the development of intelligent optimization controllers in the building sector focus on fuzzy control, predictive control, artificial intelligence control, and their combinations [74]. Fuzzy control, an intelligent control technology utilizing fuzzy logic to handle system uncertainties and ambiguities, features simple design and easy application, particularly suitable for nonlinear system control, with widespread applications in temperature control [75], ventilation systems, and lighting systems [76]. Model predictive control (MPC) represents an advanced control strategy based on closed-loop optimization with predictive models, enabling future dynamic model prediction and the feedback correction of model errors to optimize building system control [70]. MPC has been shown to systematically enhance comfort and achieve energy conservation of 15–50% in various implementations [77]. Artificial intelligence control primarily encompasses methods such as support vector machines (SVMs), k-nearest neighbours (K-NNs), artificial neural networks (ANNs), and reinforcement learning (RL) [78].

Table 3. Summary of control optimization strategies.

Year	Building Type	Regions	Climates/Seasons	Targets	Objects/ Index	Algorithm/ Approach	Tools	Reference
2021	Commercial buildings	New Zealand	Two weeks of January and July (cold and warm)	Thermal comfort	HVAC	Heuristic intelligent controller	EnergyPlus 9.1.0	[79]
2021	Community buildings	China	Subtropical monsoon climates	Adaptive thermal comfort	PMV model	KNN-based thermal comfort model	N/A	[80]
2022	Commercial buildings	China	N/A	Thermal comfort	HVAC, lighting and equipment	Sensors of OCCs	EnergyPlus 9.6.0	[81]
2022	School buildings	UK	N/A	Heat gain detection and prediction	HVAC	Faster R-CNN	IES VE 2021	[82]
2022	Office buildings	Singapore	N/A	Thermal comfort	Air-conditioning and mechanical ventilation system	ML-based MPC with an IL scheme	N/A	[83]
2023	Office buildings	Korea	Summer	Thermal comfort	HVAC	Virtual PMV sensor	Python 2020	[84]
2023	Office buildings	China	Summer	Thermal comfort and cooling loads	Temperature, humidity, solar radiation, carbon dioxide, wind direction	Improved neural network (IMFHHO-FENN)	N/A	[85]
2023	Office buildings	Germany	N/A	Thermal comfort, daylight transmission and shading control	Temperature, CO2 and illuminance	Modelica-based MPC	JModelica.org 2010	[86]
2023	School buildings	Greece	Winter and summer	Thermal comfort and visual comfort	BMS and microgrid	Decentralized building automation	N/A	[87]
2023	Office buildings	Hong Kong	N/A	Indoor environment prediction and optimal control	AHU-VAV system	GNN-RNN	N/A	[88]
2023	School buildings	Japan	N/A	Thermal comfort	HVAC	MPC-based HVAC scheduling strategy	Python	[89]
2024	Office buildings	Korea	N/A	Thermal comfort	HVAC	Reinforcement learning	N/A	[90]

presents a summary of control optimization strategies. These strategies rely on varying levels of automation, from manual adjustments to fully predictive or AI-based control systems and require different degrees of technical integration and occupant involvement. The table is structured to reflect a continuum of control complexity and intelligence. Each row corresponds to a control approach, while the columns summarize its main operational mechanisms, comfort targets, and technical or economic implications (e.g., integration requirements, precision, and scalability).

4.3. Behavioural Intervention Strategies

This category encompasses energy conservation approaches that achieve conservation through human management measures and guided user behaviour modifications rather than technological upgrades. These strategies depend on people (building occupants, facility managers, or organizational leadership) to implement actions or energy-conscious decisions [91]. In contrast to control optimization, these intervention strategies are not executed automatically through software algorithms but rather rely on human initiative and compliance. Unlike equipment modifications, they typically entail minimal or no physical alterations to the building infrastructure. The fundamental principle underlying behavioural intervention strategies is the enhancement of the “human factor’s” energy conservation potential, comprising both operational management optimization and occupant behaviour guidance [92].

Behavioural interventions complement passive and technological strategies by influencing occupant actions and energy-use habits.

Table 4. Summary of behavioural intervention strategies.

Year	Building Type	Regions	Climates/Seasons	Targets	Objects/ Index	Algorithm/ Approach	Tools	Reference
2021	School buildings	China	Annual average, maximum, and minimum temperatures are 11.5, 37.8, and −19.13 °C	Lighting management	Lighting parameters	Correlation model between human behaviour and energy consumption	Equest-3.65	[93]
2021	Office buildings	China	Winter and summer (34 degrees north latitude)	Personalized dynamic thermal comfort	Temperature, humidity, air velocity and radiation temperature	Hybrid physics-based/data-driven model	N/A	[94]
2022	Residential buildings	Germany	N/A	Environmental comfort	Preferred temperature and ventilation	OCC strategies	Interviews	[95]
2022	Residential buildings	USA, Canada, Brazil, Italy, Germany, Poland, and Singapore	Five climate zones	Occupant comfort	Occupant and operator relationships	OCC strategies	QSR NVivo v10	[96]
2023	School buildings	Italy	N/A	Human comfort and energy behaviour analysis	Actual environmental monitoring data	Digital twin and GNN	Python, Revit	[97]
2024	Public buildings	China	N/A	Indoor environment	Occupant behaviour modelling	Reinforcement learning	Semi-structured interviews	[98]

compiles commonly used behavioural approaches and their typical influence on building performance and comfort outcomes.

5. Discussion

5.1. Status Quo of Operational Energy Conversation Practices

5.1.1. Interdependencies Between Main Components and Key Comfort Indicators

Complex interdependencies have been observed between primary energy-consuming components and indoor environmental comfort indicators. It has been identified through the literature that the building operation phase plays a critical role in enhancing energy efficiency alongside the preservation of indoor environmental comfort. The four main components most strongly associated with operational energy consumption have been identified: the building envelope, HVAC systems, lighting systems, and plug loads or appliances. In parallel, three key indicators of indoor environmental comfort have been consistently recognized, thermal comfort, visual comfort, and indoor air quality, as illustrated in Figure 1.

The building envelope, functioning as a passive structural element, contributes to thermal insulation and the stabilization of indoor temperatures, thereby reducing HVAC loads. Additionally, openings such as windows and vents enable daylight penetration and natural ventilation, supporting visual comfort and indoor air quality. HVAC systems serve dual functions by regulating both thermal conditions and ventilation, ensuring that temperature, humidity, and CO₂ concentrations remain within acceptable thresholds. Lighting systems are directly responsible for meeting indoor illuminance requirements, which directly influence visual comfort.

Plug loads and appliances, including computers, kitchen equipment, and consumer electronics, are directly associated with occupant activities. While serving essential operational roles, their heat output and usage patterns may also influence thermal comfort and indoor air quality, particularly in high-occupancy environments. It can therefore be concluded that approaches focused exclusively on individual systems or comfort dimensions may fail to account for system-wide consequences within building operations. Due to the strong interdependence between the main components and key comfort indicators, the need for integrated operational management becomes evident.

5.1.2. Classification and Evaluation Criteria of Methods

A broad range of energy conservation methods has been identified in the literature, spanning from occupant-driven behaviours to advanced automated control systems. These methods are summarized by their applicability to each of the four main energy-consuming components and are classified according to their respective control or optimization strategies. This classification provides the basis for comparative evaluation and supports the formulation of a decision-making framework designed to assist practitioners in selecting context-appropriate, comfort-aligned energy conservation approaches. Energy conservation methods and their relationship to the dual objectives of energy efficiency and indoor environmental comfort are presented in Figure 2. Energy conservation methods in relation to the main components and key indoor environmental comfort indicators are summarized in

Table 5. Summary of energy conservation methods considering main components and key comfort indicators.

Main Component	Comfort Indicator	Control Type	Review Strategies
Building envelope	Thermal comfort/lighting comfort/air quality comfort	Manual Adjustment	Passive strategies/behavioural intervention strategies
HVAC systems (heating and cooling)	Thermal comfort	Conventional/Predictive/AI	Control optimization
HVAC systems (ventilation)	Air quality comfort	Conventional/Predictive/AI	Control optimization
Lighting systems	Lighting comfort	Conventional/Predictive/AI	Control optimization
Plug loads and appliances	-	Manual Adjustment	Behavioural intervention strategies

.

Following the identification of relevant method categories, an analysis of evaluation criteria for energy conservation methods was conducted. The methods were classified according to key features that potentially influence method selection in the decision-making tree. These criteria were derived from a comprehensive literature review, analysing typical research papers and compiling statistics on their methodologies, research subjects, and achieved outcomes, as shown in

Table 6. Evaluation criteria for energy conservation methods.

Environmental Characteristics	[19]	[29]	[70]	[37]	[99]	[100]	[25]	[59]	[101]	[76]	[71]	[102]
Technical implementation	✔	✔	✔	✔	✔	✔	✔	✔	-	✔	✔	✔
Implementation cost	✔	-	✔	✔	✔	✔	✔	✔	✔	✔	✔	-
Energy conversation effectiveness	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔	✔
Control precision	-	-	-	✔	-	-	-	-	-	-	✔	✔
System integration	✔	-	-	✔	✔	-	-	-	-	✔	✔	✔
Adaptability	-	✔	✔	✔	-	-	✔	✔	✔	-	-	✔
Maintenance requirements	✔	-	-	-	✔	-	-	-	✔	-	-	-
User-friendliness	✔	✔	✔	✔	-	-	✔	-	✔	✔	-	-
Regulatory compliance	-	✔	-	✔	✔	✔	-	✔	-	-	-	-
Long-term benefits	✔	✔	-	✔	-	✔	✔	✔	✔	✔	✔	✔
Scalability	✔	-	✔	✔	✔	-	-	✔	✔	✔	✔	✔
Summary	8/11	6/11	6/11	10/11	7/11	5/11	6/11	6/11	7/11	7/11	7/11	7/11

✔: Indicates that the cited reference mentions these environmental characteristics. -: Indicates that the cited reference does not mention these environmental characteristics.

. Eleven specific criteria were identified: technical implementation, implementation cost, energy conservation effectiveness, control precision, system integration, adaptability, maintenance requirements, user-friendliness, regulatory compliance, long-term benefits, and scalability.

This status quo analysis offers a structured foundation for comparing available methods. To operationalize the findings, the next step involves translating this knowledge into a practical decision-making tree. A targeted survey is employed for the development of a decision tree intended to support the selection of energy conservation methods under real-world constraints and indoor environmental comfort requirements.

5.2. From the Literature to Application: A Decision-Making Tree Based on Survey Results

5.2.1. Key Survey Insights

While the literature survey identifies potential methods for energy conservation, decisions must ultimately be made by practitioners such as building owners and facility managers, based on specific contextual needs. To bridge this gap, a questionnaire-based survey was conducted among professionals and researchers in the field of building energy. The purpose of the survey was to gather insights on the perceived effectiveness and practicality of various energy conservation methods, as well as the relative importance of different decision-making factors in real-world projects. By analysing these responses, the discussion is grounded in current professional perspectives, thereby supporting the development of a decision-making tree that is both theoretically sound and practically applicable.

To complement the literature review with practical insights, a structured questionnaire survey titled “Survey on Influencing Factors for Selecting Energy Conservation Methods in Building Operation” was designed and administered. The questionnaire was divided into four sections, comprising a total of 27 questions. The first section gathered basic demographic and professional background information. The second section addressed the main components that influence operational energy consumption, namely, the building envelope, HVAC systems, lighting systems, and plug loads or appliances. The third section examined key indicators for indoor environmental comfort, including thermal comfort, lighting comfort, and indoor air quality. The fourth and core section of the questionnaire evaluated the environmental and technical characteristics of various energy conservation methods, aligned with the earlier identified evaluation criteria (e.g., technical implementation, control precision, and user-friendliness, etc.).

This questionnaire was designed to complement the literature review by validating and structuring the inter-relationships among main components, key comfort indicators, and energy conservation methods identified through the review. The survey supports this objective by further exploring these connections and informing the development of a decision-making tree to assist in the selection of appropriate methods during building operation.

Multiple question formats were used, including single-choice, multiple-choice, and matrix-based Likert scale ratings. Prior to official distribution, a pilot test was conducted on a small sample group of respondents. Based on the feedback received, modifications were incorporated to improve the clarity and interpretability of both questions and answer options. The final version of the questionnaire is included in Appendix A.

A total of 66 valid responses were collected. Figure 3 illustrates that respondents were drawn from a broad spectrum of the building sector, including professionals involved in design, construction, operation, research, and industry. This diversity contributes to a balanced and representative depiction of current practices and perceptions regarding energy conservation methods in building operation.

The survey results provide several important insights that guide the construction of our decision-making tree:

1. Automation: Broad alignment was observed between participant responses and the findings from the literature, particularly regarding the effectiveness of automated, intelligent systems for operational energy conservation. Building automation was identified as the most effective category, with approximately 74% of respondents selecting it as delivering significant energy savings in practice. In contrast, purely manual adjustment and behavioural interventions were considered significantly effective by a smaller proportion—approximately 44% and 38%, respectively—suggesting a recognition of their benefits alongside inherent limitations. These perceptions support the proposed classification and indicate a general expectation for superior outcomes from automation methods, in alignment with prevailing trends in smart control methods.

2. Smart control: When asked about future priorities in building energy efficiency, the most frequently selected responses were “smart control and data-driven methods” (65%), followed by “improving energy use efficiency of systems” (48%). These results indicate that emphasis should be placed on advanced control strategies within the decision framework, as these were most frequently perceived as the principal paths forward. Simpler or conventional methods, while acknowledged, were not widely regarded as the primary focus for future progress.

3. Main components: The survey reinforced that not all building components con-tribute equally to savings—HVAC systems and the building envelope emerged as the top-priority systems. In a multi-choice question, 65% chose efficient HVAC systems and 62% chose high-performance envelopes as having the greatest energy conservation potential, whereas lighting systems (56%) were slightly behind, and other components trailed even more. This confirms that our decision-making tree should generally steer users to con-sider methods for HVAC and envelope first, unless a particular context dictates otherwise.

4. Comfort first: Any energy intervention is required to maintain acceptable levels of indoor environmental comfort. Comfort was overwhelmingly prioritized by respondents, with 85% indicating that comfort considerations are either “important” or “very important”, and no respondents indicating it was unimportant. When asked which comfort dimensions were most significant, thermal comfort and indoor air quality each received selections from 80% of participants, with lighting comfort (visual comfort) following closely at 73%. These findings suggest that strategies compromising thermal conditions or air quality in pursuit of energy savings would likely be considered unacceptable. Furthermore, the findings reinforce the importance of advanced control methods—such as comfort-driven HVAC systems—that enable energy conservation without diminishing occupant comfort. Comfort constraints have therefore been explicitly embedded within the decision-making tree as a non-negotiable checkpoint.

5. Preferences and trade-offs: By evaluating different categories of energy conservation methods against multiple criteria, the survey revealed several perceptual trade-offs that must be accounted for in the design of the decision-making tree. Manual adjustments were rated highly for ease of implementation (an average technical simplicity score of 3.7/5) and cost-effectiveness (3.17/5, where higher values indicate lower cost), indicating a perception of simplicity and affordability. However, these same methods received lower scores for control precision (2.8/5) and system integration (2.85/5), suggesting concerns about their suitability for complex or dynamic building environments. In contrast, intelligent building control systems were rated highly in terms of energy conservation effectiveness (3.77/5), control precision (3.9/5), integration potential (3.95/5), and long-term benefits (3.9/5). These advanced methods were perceived as more technically complex (an implementation ease score of 2.98/5) and relatively costly (3.09/5) in comparison to simpler alternatives.

By evaluating each energy conservation method against a consistent set of criteria, a horizontal comparison can be established across otherwise dissimilar strategies. This provides a basis for an informed decision-making tree; rather than relying on a single dimension, trade-offs can be considered, for example, a method may offer high energy conservation but involve significant implementation costs and maintenance complexity, in contrast to a less impactful strategy that is inexpensive and easy to apply. Simpler methods, such as manual adjustment or conventional controls, have been observed to perform well in terms of ease of implementation and cost-effectiveness but tend to be limited in control precision and long-term benefits. Conversely, advanced methods, such as AI-based control systems, are typically rated highly in terms of effectiveness and precision but are associated with greater technical complexity and cost. This multi-criteria perspective provides the foundation for selecting the most appropriate solution, based on the specific constraints and performance goals of a given building.

5.2.2. Decision-Making Tree Generation

Using the findings outlined above, a decision-making tree has been constructed to assist practitioners in selecting the most suitable energy conservation methods under environmental comfort constraints. The decision-making tree is designed as a structured flowchart that guides practitioners through a series of key considerations, progressively narrowing the options based on the specific conditions and priorities of a given project. The structure of this decision-making tree is outlined as follows:

1. Overall objective: The top of the decision tree is defined by the dual objective of reducing operational energy consumption while maintaining acceptable levels of indoor environmental comfort. This dual emphasis reflects a broader paradigm shift from energy-centric to user-centric building management.

2. Main components: The second layer breaks down energy use into four main energy-consuming components within building systems: the building envelope, HVAC systems, lighting systems, and plug loads or appliances, which now represent an increasingly significant portion of total energy use.

3. Key comfort indicators: These indicators serve to define non-negotiable boundaries for acceptable indoor conditions and include thermal comfort lighting comfort and indoor air quality comfort. Each indicator is mapped to the relevant comfort domains, forming the contextual basis for selecting appropriate strategies.

4. Energy conservation methods: This layer presents candidate methods, differentiated by their level of automation, intelligence, and user engagement. Categories include manual adjustment (user-driven actions), conventional control (e.g., schedules, sensors, PID loops), predictive control (e.g., model predictive control, forecast-based strategies), AI-based control (e.g., reinforcement learning, fuzzy logic), behavioural interventions (education, feedback, or policy measures), or hybrid measures (e.g., shading devices, natural ventilation). These methods are not mutually exclusive and may be layered for enhanced performance.

5. Evaluation criteria for methods: In the final layer, each method is assessed across multiple dimensions, based on findings from both the literature and the survey. Evaluation criteria include technical implementation, cost, effectiveness, control precision, and other relevant factors. These dimensions enable horizontal comparisons among strategies and help tailor decision-making to specific building contexts.

The decision-making tree facilitates a multi-criterion, system-aware, and comfort-constrained approach to the selection of energy conservation methods, thereby bridging the gap between academic research and practical engineering deployment. The capability of each method to address specific performance objectives is considered a primary factor influencing its suitability. Based on the scoring of methods across various characteristics, a decision-making tree has been generated to support the structured selection of appropriate energy conservation methods. The tree structure represents the logical progression of decision-making across five hierarchical levels and is illustrated in Figure 4.

6. Conclusions

A comprehensive review was conducted to examine energy conservation methods applicable during the operational phase of buildings, with particular emphasis on maintaining indoor environmental comfort. Through a systematic literature analysis, four main energy-consuming components, the building envelope, HVAC systems, lighting systems, and plug loads or appliances, were identified as critical targets for operational efficiency improvements. Concurrently, three key environmental comfort indicators, thermal comfort, lighting comfort, and indoor air quality, were recognized as essential constraints in evaluating energy conservation methods.

Energy conservation methods were classified into passive design strategies, control optimization strategies, and behavioural intervention strategies, each assessed for its impact on energy performance and comfort outcomes. A multi-criteria evaluation framework was established, encompassing technical, economic, and user-related indicators, including implementation cost, energy conservation effectiveness, control precision, system integration, and long-term benefits.

To support practical implementation, expert interviews were conducted to capture stakeholder preferences, constraints, and real-world practices concerning energy conservation in existing buildings. The results revealed that thermal comfort was consistently rated as the highest priority, followed by visual comfort and air quality. These preferences were quantified and incorporated into the construction of a decision-making tree designed to guide energy conservation interventions while preserving occupant comfort. The tree integrates three strategic categories, passive design measures, control optimization, and behavioural interventions, and provides a step-by-step selection path based on building conditions and comfort requirements. This framework enables building managers and decision-makers to identify suitable conservation strategies without extensive retrofitting, ensuring that operational efficiency is achieved alongside acceptable indoor environmental quality.

The findings contribute to a deeper understanding of how energy efficiency and occupant comfort can be jointly addressed during building operation. The proposed framework is intended to offer practical value for engineers, facility managers, and policymakers by enabling informed, context-sensitive decision-making tree in energy conservation efforts. Future research will move beyond the internal factors reviewed in this study—namely building components and environmental comfort indicators—to incorporate external influences such as economic conditions, climate zones, and cultural contexts. These factors also play a critical role in both the selection of energy conservation methods and the perception of indoor comfort. Integrating such contextual variables will further enhance the relevance and robustness of the proposed decision-making framework.