A review of building parameters

(1)

Journal of Building Engineering 35 (2021) 102087

Available online 11 December 2020

A review of building parameters ’ roles in conserving energy versus maintaining comfort

Rashed Alsharif

^a^,^b^,^*

, Mehrdad Arashpour

^a

, Victor Chang

^a

, Jenny Zhou

^a

aDepartment of Civil Engineering, Monash University, Australia

bDepartment of Construction Engineering at AlQunfudah, Umm Al-Qura University, Saudi Arabia

A R T I C L E I N F O Keywords:

Energy performance Multi-objective optimisation Energy consumption Occupant behaviour Simulation

Predicted mean vote (PMV)

A B S T R A C T

Building operations constitute a significant amount of energy consumption globally. Although approaches to optimise energy consumption have emerged, maintaining comfort for building occupants has been a challenge.

To address this challenge, the current paper presents a thorough review and analysis of advancements related to optimising energy consumption in buildings while maintaining occupants’ comfort. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, this review identifies challenges and future research opportunities in this domain. The findings reveal that three main elements contribute to a building’s overall energy performance: they are occupant behaviour, building envelope, and building energy systems. The literature about the three elements is discussed, based on latest trends, modelling approaches, and existing challenges. This review paper contributes to the body of knowledge by highlighting recent advancements, identifying research challenges, and discussing potential areas for future research.

1. Introduction

Despite the fact that the building sector consumes one-third of global energy, it has not achieved satisfying Indoor Environmental Quality (IEQ) [1]. This poor efficiency also results in multiple environmental and economic issues such as CO2 emissions and depletion of natural resources [2]. Although Energy Conservation Measures (ECMs) have advanced over past decades [3], it remains a challenge to address interdependencies between providing a comfortable indoor environment and energy consumption [4]. This challenge has been difficult to overcome due to the intangible nature of comfort sense that varies among occupants. Therefore, trade-offs have been made either in comfort or energy conservation. Fortunately, with the emergence of technological advancements, such as occupancy detection techniques and dynamic building energy system (BES) controls, it is now easier to achieve optimal balance points [5].

Although the current focus of mainstream research is to implement the abovementioned techniques to achieve comfortable energy-efficient buildings, the majority of current ECMs under development try to optimise the costs of energy consumption. This approach has created a gap between utilising technological advancement in buildings and

increasing Indoor Environment Quality (IEQ) thermally, visually and acoustically [6]. There is potential to adopt ECMs efficiently if they are developed based both on economic factors and on occupant comfort.

This review paper focuses on the previously mentioned challenges by conducting a systematic review of the literature about recent de- velopments in building energy conservation techniques, and their contribution to improved occupant comfort. The review paper aims to achieve the following objectives:

• Explore and categorise the existing literature of energy consumption in buildings.

• Identify challenges in achieving comfortable and energy-efficient buildings.

• Recommend future research opportunities in the field.

In the remainder of the review paper, three sections are presented.

First, the method used to acquire journal articles is discussed. Second, analysis of the domain is highlighted. Finally, a discussion section is presented which includes challenges and future research, followed by a conclusion.

* Corresponding author. Department of Civil Engineering, Monash University, Australia.

E-mail addresses: [email protected] (R. Alsharif), [email protected] (M. Arashpour), [email protected] (V. Chang), jenny.

[email protected] (J. Zhou).

Contents lists available at ScienceDirect

Journal of Building Engineering

journal homepage: http://www.elsevier.com/locate/jobe

https://doi.org/10.1016/j.jobe.2020.102087

Received 29 August 2020; Received in revised form 6 December 2020; Accepted 8 December 2020

(2)

2. Research method

In this section, the method used to conduct the systematic review is discussed. The review is done using Preferred Reporting Items for Sys- tematic Reviews and Meta-Analyses (PRISMA) [7] to explore the literature about energy consumption conservation in buildings and occupant comfort. In terms of database, Scopus by Elsevier is chosen for literature exploration and analysis.

At first, the two most general terms are included in the search attempt, which are “Building” and “Energy”. Then, energy-related terms such as “Optimisation”, “Minimisation” and “Reduction” are included.

After that, building-related keywords such as “Envelope”, “occupant” and “Systems” are used to shortlist relevant papers. Finally, operation- related keywords, for example, “Visualisation”, “Modelling” and “Esti- mating”, are added to the search criteria. The keywords are chosen carefully to ensure comprehensive, yet specific, search results are obtained. The search attempt is designed to use terms within the same keyword category by using the Boolean operator OR, where AND is used between different keyword categories. This comprehensive approach is selected to accommodate the interrelation between energy, buildings and conservation approaches.

Fig. 1 describes the process of acquiring the related papers using Nomenclature

IEQ indoor environmental quality ECM energy conservation measures BES building energy systems

PRISMA preferred reporting items for systematic reviews and meta- analyses

HVAC heating, ventilation, and air-conditioning OB occupant behaviour

BEP building energy performance BIM building information modelling ABM agent-based modelling IoT internet of things PCM phase-change materials

TC thermal coating

MILP mixed-integer linear programming ANN artificial neural network

LCC life-cycle cost PMV predicted mean vote UDI useful daylight illuminance FAST Fourier amplitude sensitivity test RSA roof solar absorptance

WSA wall solar absorptance DPC data-predictive control MPC model-predictive control EUI energy use intensity nZEB net zero-energy building PPD predicted percentage dissatisfied

Fig. 1. The process of acquiring related literature using PRISMA method.

(3)

PRISMA method. It also shows the number of journal articles resulting from each step in the process. As Fig. 1 shows, a total of 650 journal articles were identified. Screening of title and abstract was conducted to exclude articles that do not discuss both energy and comfort, and 91 articles were shortlisted based on abstract relevance. In the eligibility step, 11 records were excluded because they are overwhelmed by other articles. As a result, a total of 80 journal articles were included for the analysis presented in section 3. The analysis involves identifying the- matic streams and the adoption of occupant comfort in each stream.

3. Analysis of the domain

After analysing the determined domain, it was apparent that three main elements play the primary role in driving energy consumption in buildings, namely, occupant behaviour, building envelope, and building energy systems (as shown in Fig. 2). This section highlights the eligible literature for each element.

3.1. Occupant behaviour

Occupant behaviour (OB) can be defined as the interaction between human and buildings related to energy consumption that can be described by occupancy and control of devices and systems such as lighting, windows, blinds, and heating, ventilation, and air conditioning (HVAC) [8]. OB is related to occupant comfort, and is a major and centric element of building interactions (Fig. 3) which also affects building energy performance (BEP). OB is mainly driven by comfort correlated with environmental parameters such as climate [9] and indoor air quality [10]. For designing an efficient energy building, it is important to understand occupants’ needs and behavioural patterns to provide the adequate balance between energy consumption and a comfortable indoor environment [11]. This section provides a summary of OB literature about trends, modelling and challenges.

3.1.1. Trends

Current OB research practice includes highlighting its importance and modelling approaches so that it becomes interpretable. Although it is challenging to model a highly stochastic factor like OB, the importance of such approaches is high for minimising the acknowledged wide gap between predicted and actual BEP [12]. For example, colour coding visualisation of a building information model (BIM) based on energy consumption can assist building managers to cluster zones, such as apartments, based on the residents’ energy consumption behaviour. This approach would help in determining the feasibility of ECMs in terms of occupants’ comfort before renovation [13]. Another approach to defining OB is to create a building ranking procedure based on a multistage clustering process that considers OB. It was shown that in addition to the benefit of defining OB, a building ranking procedure motivates occupants to minimise their energy consumption [14]. In addition, classifying OB based on the levels of details was suggested in

Fig. 2.The main elements in building energy consumption HVAC: Heating, ventilation, and air conditioning.

Fig. 3. Occupant centric interaction.

(4)

the literature. In this approach, one of 3 levels of complexity “A, B and C” is considered based on the required modelling approach. For example, level A is the least complex model which describes only essential information about the OB; therefore, it is suitable for large-scale investigations. By contrast, level C entails more complex description of OB which can be used in micro-level modelling approaches [15]. Moreover, recent methods for identifying occupancy behaviour use data from Wi-Fi devices to record the status of an occupant and create an occupancy scheduling profile [16]. The profile can also be put in an analytical model, together with the energy consumption pattern, to identify which occupants are highly-energy intensive by observing peaks [17]. It can be observed from the literature that advances in OB research are correlated with implementing technology in buildings.

3.1.2. Modelling

Although it is important to define OB, it is more important that OB is incorporated in the BEP analysis. As an example, probabilistic models of OB have been used to increase the accuracy of predicting energy consumption in HVAC systems. Such models can be created by observing OB and produce most likely events, based on historical data, which can then be incorporated into simulation tools [18]. This avoids overestimating HVAC power consumption. Additionally, Agent-based modelling (ABM) has been used widely in OB simulation. ABM is a model that includes agents which take decisions according to a set of assumptions or com- mands that mimic a real case scenario [19]. The key to effective ABM is to identify the logic behind the variable and interrelated interactions among occupants and building energy systems [20]. Agents, which represent occupants, can be designed to make decisions that increase their comfort; thereby, ECMs that assure occupant comfort can be properly incorporated in the simulation process [9]. Finally, a sophisticated approach has been developed which incorporates Wi-Fi occupancy detection in the building energy management systems using ensemble classification algorithms for automated system controls based on occupancy [21]. It is clear that the OB field is a new paradigm that can be used to improve occupant comfort within the borders of energy-efficient buildings. Because of the underlying potential, more work to define and model OB is needed to deepen comprehension of real situations. For example, potentially promising solutions to the challenge of different occupants’ needs may emerge from personalised comfort models using the Internet of Things (IoT) and machine learning to anticipate individuals’ comfort requirements and act to adjust indoor environment accordingly [22]. This may be especially useful given that the same occupant has different requirements at different times.

3.1.3. Challenges

The purpose of understanding OB is to comprehend the underlying relationship between energy demand, usage, and forces that produce variations [23]. Forces that produce variations, or “triggers”, are attributed to psychological, biological, social, time, and physical environmental factors, and to building properties. For example, OB differs based on the building type; that is, residential against office buildings [24]. Underestimating OB in residential buildings located in cold climates can lead to minor errors in BEP simulation results. However, it is important to properly estimate OB in hot climate zones so that energy modelling results, especially cooling loads, are reliable and no major model errors occur [25]. Additionally, miscalculations of OB can pre- vent energy-efficiency benefits from being obtained after envelope renovation. Occupants were found to increase the set-point temperature of HVAC systems after renovation more than before [26]. This would decrease major energy savings during winter [27], and reduce savings even more during summer in sub-arctic regions [28]. Innovatively, a new paradigm has come to the surface suggesting personalised comfort models. These models have several advantages over the currently used PMV and adaptive models. Firstly, they do not require very specific inputs as they adapt and relearn based on the specific assigned occupant.

Moreover, they allow for modification to their respective set of input variables. However, several challenges remain in front of large-scale application of this concept such as generalisation, collection of occupant feedback, and resolving differences in thermal preferences among occupants in shared places. Therefore, understanding occupants’ needs and anticipating their behaviour is crucial for energy conservation measures to work effectively. The literature has shed light on the importance of OB in the BEP field. However, due to the stochastic nature of occupants and their different preferences, it is difficult to deal with OB in a manner similar to other elements of Building Energy Systems (BES) and building envelope. Hence, OB should be approached differently and with greater robustness.

3.2. Building envelope

Building envelope is one of the most influential factors on overall efficiency of buildings when it comes to occupant comfort and energy consumption. Envelope is defined as the barrier that separates the building’s indoor environment from the outdoor environment [29].

When energy systems are activated to provide a comfortable indoor environment, the envelope’s role is to maintain the desired comfort level for an extended period [30]. The capability of the envelope to isolate the indoor environment from the outdoor environment plays a major role in balancing energy consumption and occupant comfort [31,32]. The following sections analyse current trends, modelling methods and existing challenges, highlighting the latest approaches in the literature of this field, as shown in Fig. 4.

3.2.1. Trends

Efforts have been made to discover new products and materials that could improve the efficiency of building envelopes. Phase change materials (PCM) have been thoroughly studied recently to identify their benefits [33–35]. PCMs are materials capable of storing heat by changing their state from solid to liquid and vice versa, and thus improving the envelope’s ability to isolate indoor environment from outdoor environment [36,37]. Hu and Yu [38] implemented thermo- chromic coating (TC) in the roof together with PCM in walls (TC roof-PCM wall). By simulating the building for different climate zones, the TC roof-PCM wall could provide energy consumption savings of up to 19% compared to traditional envelops, especially in warm regions.

Moreover, significant increase can be added if such roofs are inclined at an angle of 2^◦[39]. Another important part of the building’s envelope is windows, which allow for an adequate amount of heat gain and daylight to provide a comfortable indoor environment [40]. They also can be a weak point in building design that imposes excessive heat gain and illuminance [41]. Kirankumar et al. [42] studied various combinations of double-glazed reflective windows to measure their impact on solar heat gain. It was found that the combination of reflective glass colours can significantly impact energy consumption and annual costs of heating/cooling. Additionally, an important finding was that the outer layer of reflective glass is the most critical part of the double-glazed window with an air gap. Besides previously mentioned studies, Franchini et al.

[43] monitored the performance of the first building in Dubai certified by the Passive House Institute. Large windows were placed to overlook a patio with a shaded garden, which is useful for avoiding direct solar gains in highly solar radiative regions.

3.2.2. Modelling

Optimisation of the building envelope has been used extensively in the last decade. The most important part of the optimisation model is objective functions, which can be achieved using several methods such as Mixed-Integer Linear Programming (MILP) [44], dynamic simulation software tools [45] and artificial neural networks (ANN) [46]. Because dynamic simulation tools, such as EnergyPlus and TRNSYS, are more robust and require less effort in developing research methodologies, they have been the popular choice for researchers [47]. A model with

(5)

several decision variables related to building envelope has been developed by Lu et al. [48] with an objective to minimise annual energy consumption. Higher-level optimisation approaches have been investigated by Hong et al. [49], Kirimtat et al. [50], Wu et al. [45], Sghiouri et al. [51], and Lesnik et al. [52], when models were optimised for multiple objectives in parallel with BEP such as Indoor Environmental Quality (IEQ), Life-Cycle Cost (LCC), initial investment cost, Predicted Mean Vote (PMV) for thermal comfort, and Useful Daylight Illuminance (UDI) for visual comfort. On a broader level, Waibel et al. [44] and Wu et al. [53] conducted optimisation approaches that included BES elements as decision variables, along other building envelope factors such as building’s geometry, surface-to-volume ratio, and orientation. By coupling BES with the building envelope, it was possible to achieve more homogenous outcomes. It can be concluded that this area demonstrates a productive developmental trend and has improved significantly during the last decade. However, underlying potential and room for improvement remain.

3.2.3. Challenges

Recently, researchers emphasised the idea of exploring sensitive aspects of the building’s envelope, thereby efficiently guiding improvement efforts. Such an idea has attracted researchers, especially when considering that different regions might impose different sensitive aspects. Through two published papers, H. Li et al. [54,55] explored the sensitivity of key envelope parameters in sub-tropical regions and the robustness of optimisation. In Refs. [54], 29 key envelope parameters were inputted through a simulation-optimisation iterative process using EnergyPlus and MATLAB. Three different sensitivity analysis methods were used; namely, regression method, Morris method, and Fourier Amplitude Sensitivity Test (FAST). Six parameters found to be highly sensitive are building orientation, roof solar absorptance (RSA), window-to-wall ratio, wall solar absorptance (WSA), window solar heat gain coefficient, and overhang projection ratio. The study also suggests RSA and WSA are the most sensitive parameters when considering thermal comfort in sub-tropical region buildings without heating

systems. In Ref. [55], three different objectives are suggested in the optimisation process. The objective which considers discomfort as a variable led to the most robust design as it makes the optimal trade-off between thermal discomfort and energy consumption. Resilient designs may not produce the best performance in the simulations; however, they are preferable as they incur less uncertainty and highly robust models.

Despite the development in the building envelope optimisation domain, there has been little work in detailing the importance of exploring sensitive factors in the building envelope. Few researchers have studied the sensitivity of the building envelope for specific regions, as H. Li et al. [54,55] have done. In addition, BIM-based building energy modelling (BIM-based BEM) technique has not been used to optimise the building envelope. Such a technique would be beneficial in time saving and minimising the risk of human error. As the building envelope is made of building materials, it is challenging for the BEP area to cope with innovation in the building materials industry. Therefore, multi- disciplinary approaches are required to exploit or guide innovation.

3.3. Building energy systems

Building Energy systems (BES) includes power generation systems, lighting, appliances, HVAC, and any other system in a building that consumes energy to provide certain services and a comfortable indoor environment [56] (see Fig. 5 for a schematic overview). BES remains a constant requirement for balancing indoor comfort and energy efficiency. This section includes current trends, modelling approaches, and challenges.

3.3.1. Trends

Similar to building envelopes, PCMs have also been used in BES. As an example, paraffin modules can be inserted into water storage tanks to maintain the water temperature level, or so-called latent temperature.

Experimentation shows that minimum water temperature of 25 ^◦C in the water outlet can be maintained for longer than conventional sensible water heating systems [57]. Therefore, PCMs can provide the option of Fig. 4.Building envelope literature break- down.

BES: building energy systems OB: occupant behaviour

BIM-based BEM: building information model-based building energy modelling BEP: building energy performance.

(6)

maintaining the same performance as conventional systems by using storage systems of a smaller size. Alternatively, higher performance can be achieved by using storage tanks of the same size together with PCMs.

Another trend in BES is lighting that integrates solar lighting with artificial lighting to provide a constant level of illumination with minimal energy consumption [58]. Moreover, a building’s façade and windows can be exploited to produce energy. Building-integrated photovoltaic systems have been tested to demonstrate their feasibility. An open research challenge remains to develop an appropriate ventilation control scheme for photovoltaic double skin façade (PV-DSF) [59]. Finally, IoT has been widely studied in the BES domain. An integrated system of sensors, cloud-based data logging, and algorithm-based decision making, can enable remote and automated controls [60]. Since manual control by occupants or building managers is prone to human error, the best results in terms of energy efficiency or occupant comfort might not be obtained [61]. IoT exploitation results are highly dependent on the algorithm used, because algorithms play the role of the decision maker [62]. Alternatively, energy behaviour index can be modelled for regular occupants using their presence when connected to local Wi-Fi probes and energy use intensity during that period [17]. This is important to accommodate for consuming intensity in office buildings to allocate highly intensive consumers in different shifts or working periods.

Another Trend concerning the dynamic pricing of electricity to optimise the reduction of energy costs during summer while keeping the user comfort in the comfortable range using PMV index [63].

3.3.2. Modelling

In general, BES is assessed based on the amount of energy consumed to provide a comfortable indoor environment [63]. Therefore, approaches in this area should aim to minimise energy consumption and maximise occupant comfort. An example is combining energy supply optimisation, involving different energy sources such as renewables and electricity from the grid, and demand side management to achieve the required level of operation [64]. Additionally, multi-objective optimisation has been used to determine the optimal design mix of renewable energy systems which minimises both energy consumption and investment cost [65]. Similarly, optimal battery investment has been investigated using different optimisation models [66]. However, none of the previously mentioned studies has included occupant comfort as an objective, despite its importance in assessing BES performance. Few studies have promoted including occupant comfort as an objective when

optimising BES [67]. Data Predictive Control (DPC) is used to automate the control of room temperature based on historically collected data from the occupants’ manual control behaviour. DPC can contribute to major savings in cooling energy with fewer violations of comfort con- straints [68]. It is worth mentioning that the occupant comfort factor is inherently included in DPC by representing the behaviour that provides a comfortable indoor environment. PMV has been used widely as a quantification of occupant comfort [69], the inclusion of which as an objective has been done in parallel with minimising operational costs to create an operational schedule that dynamically changes with energy pricing and external weather conditions [63]. Despite the challenges of quantifying multiple variables such as metabolism, indoor air velocity, and clothing level, optimising BES operation to maintain a specific range of comfort with minimal cost is a major step in achieving occupant comfort in operationally economic buildings [70].

3.3.3. Challenges

Several gaps and opportunities exist in the BES research domain.

First, little work has been done to identify critical parameters of BES for different scenarios and regions. BES optimisation to achieve a robust configuration is computationally expensive and requires investments in sophisticated hardware and software [71]. However, having a comprehensive understanding of a specific case, such as OB scheduling and the building envelope, reduces the number of variables and required computational capabilities [72]. Second, more in-depth research is needed to include occupant comfort in the BES optimisation process.

Sole focus on the energy efficiency of BES prevents installation of automated controls becoming common practice. Such a challenge can be addressed by embedding DPC to automate control of BES [73]. How- ever, DPC is an individual design-procedure that currently needs to be conducted for every specific building, and no commercial tools are available to ease the implementation process [74]. Finally, adaptive BESs can be the focus of future research due to their ability to make dynamic trade-offs between energy efficiency and a comfortable indoor environment [75]. However, due to the amount of information acquired about occupants to assess their behavioural profiles, privacy concerns rise. The amount of data being held in one place is concerning because it is prone to digital attacks. In addition, using automated BES controls based on occupant comfort assessment is a challenge due to the nature of current used comfort assessment tools, which raises BES efficiency concerns [76].

Fig. 5. Building energy systems schematic overview.

(7)

It is crucial for BES to adapt and adjust to both occupant requirements and outdoor climate conditions, so that the optimum balance is achieved between occupant comfort and energy consumption [77].

4. Discussion

This section of the review paper identifies several challenges, and also opportunities, for future research. In addition, Table 1 provides a summary of categorisation and approaches represented in the articles selected for this review paper. The table includes a categorisation of articles based on the three elements of energy consumption in buildings, with identification of the used approach. The table also serves the paper’s aim of identifying and developing the existing body of knowledge on the topic of energy consumption in buildings.

4.1. Challenges 4.1.1. Climate

The difference between outdoor climate condition and desired indoor condition is the major factor in BES energy consumption [82].

Therefore, understanding climate profiles is essential in identifying ECMs to assure occupant comfort and minimal energy consumption. It was found that energy consumption and occupant comfort are sensitive to different parameters in building envelopes in different climate zones [54]. Moreover, provided solutions to achieve nearly Zero Energy Buildings (nZEB) are different in heating-dominated and cooling-dominated climate zones [81]. In addition to conventional buildings, climate impacts on so-called “Green Buildings”. It is shown that green buildings in the USA have different Energy Use Intensity (EUI) than green buildings in China. To add more complexity, different IEQ satisfaction levels are reported by occupants in the two regions [79].

Therefore, it is crucial to understand climatic dynamics thoroughly so that ECMs can be designed accordingly. This is especially important when considering that an average building’s lifespan is more than 50 years [86]. Due to climate change, cooling loads have been shown to increase significantly during the 50-year lifecycle [80].

4.1.2. Occupancy detection

The occupancy schedule has been shown to be a parameter that is frequently different among buildings and results in major differences in energy consumption [23]. Therefore, it should be approached and implemented carefully in the energy conservation process. As Wi-Fi probe-based occupancy detection becomes a potentially promising trend due to cost savings, it is challenging to maintain the occupants’

privacy while allowing for detecting their presence [21]. Moreover, such an approach for detecting occupants is not necessarily accurate enough.

Because occupants can interact with Wi-Fi probes from multiple devices at the same time, double counting is an issue [17]. In addition, devices such as cell phones and computers usually enter the idle mode to save energy. This might affect the connection between devices and the Wi-Fi probe, resulting in incorrect occupancy data [16]. Finally, “smart homes” that are occupancy-based interactive homes raise data security concerns and potential for hacking. Hence, the challenge of adopting occupancy detection in such environments is to have assured cyber-security [78].

4.1.3. Comfort models

Comfort issue has not been addressed thoroughly in the energy modelling approaches, and this could be the reason for a gap between predicted and actual energy performance of buildings [87]. For example, many recent studies use PMV and Predicted Percentage of Dissatisfied (PPD) models to assess occupant thermal comfort [49,63,85, 88]. Numerous studies have shown that accuracy of such models is not precise for modern occupants’ lifestyles and might not reflect realistic comfort sense [76,89]. Therefore, recent studies considered automated

Table 1

Comparative review of key areas addressed, and their approaches.

Source Occupant

Behaviour Envelope Building Energy Systems

Approach/

technique Journal

[10] x . . Innovative IEQ

sensor Building and Environment

[78] x . . Literature

analysis Renewable and Sustainable Energy Reviews

[15] x . . OB

categorisation Energy and Buildings

[18] x . x Probabilistic

model of OB Energy and Buildings

[20] x . . ABM Energy and

Buildings

[13] x . x BIM

visualisation Applied Energy

[30] . x . Literature

analysis Renewable and Sustainable Energy Reviews

[44] . x x Co-simulation

optimisation Applied Energy

[21] x . x Wi-Fi

occupancy detection

Applied Energy

[58] . . x Innovative

passive lighting system

Energy and Buildings

[17] x . x Wi-Fi EUI

detection Journal of Building Engineering

[49] x x . Multi-

objective optimisation

Energy

[64] . . x BES

optimisation Energy and Buildings

[50] . x . Multi-

Solar Energy

[68] . . x MPC/DPC Energy and

Buildings

[65] . . x Multi-

Applied Thermal Engineering

[66] . . x Battery

investment optimisation

[45] . x . Multi-

Energy for Sustainable Development

[53] . x x Multi-

Applied Energy

[14] x . . OB

categorisation Energy and Buildings

[43] . x x nZEB Energy and

Buildings

[54] . x . Sensitivity

analysis Applied Energy

[23] x x x Literature

analysis Energy and Buildings

[16] x . . Wi-Fi

occupancy detection

Building and Environment

[55] x x . sensitivity

analysis/

optimisation

Applied Energy

[79] x . . Literature

[22] x . x Innovative OB

models Building and Environment (continued on next page)

(8)

control of BES based on inherently occupant-comfort-inclusive models such as DPC, and ANN [46,68]. Despite the potentials of such approaches, they require historical data and involve the possibility of being subjective in selecting similar occupants in terms of OB profiles. In addition, most of comfort assessment models only assess the thermal comfort rather than including visual [90] and acoustic aspects of comfort. This might be attributed to the impact thermal comfort make to the occupant.

4.2. Future research

The building energy consumption domain offers multiple future research opportunities. The authors believe that these subjects will be major interests of building energy conservation and indoor environment field in the upcoming future. Fig. 6 highlights how these subjects contribute to overcoming the previously mentioned challenges.

4.2.1. Personalised comfort models

Personalised comfort models, such as DPC, are a promising area to investigate due to the potential collateral benefits for occupants and energy consumption. The comfort assessment methods currently used require very specific inputs and lack the ability to adapt or re-learn [22].

While in personalised comfort models, the approach is designed fundamentally to detect and assess the criteria of the comfortable indoor environment that the occupant prefers, and enforce the most conser- vative combination of energy systems control schedule – depending on the embedded information of building envelope – to create that comfortable indoor environment.

4.2.2. Critical parameters in building envelope

Critical parameters of building envelopes should be explored more thoroughly. Adopting such research direction has potential to make energy conservation measures effective. This is particularly true when considering that critical parameters in building envelopes differ based on climate conditions and geographical location [54]. This is because the approach is multi-objective and criticality is highly dependent on occupancy schedule, weather profile, and cost of energy [44]. Therefore, because every building is a unique case, critical parameters in building envelope is expected to be investigated in large-scale samples of either similarly located cases, or similarly occupied-behaving cases. This is to enable statistical analysis of building envelope parameters and their critical correlation based on location or occupants.

4.2.3. Energy storage systems

Energy storage systems, such as batteries, increase the ability of buildings to become less dependent on electricity grid. This is attributed to the ability of such systems to provide energy even when renewable energy systems are not fully reliable during the day [66], and ultimately to become nZEB. By doing so, energy conservation becomes less critical because it is renewably generated, and turns into a threshold instead of an objective. This leaves the approach with only one objective to optimise, which is occupant comfort.

4.2.4. Climatic dynamics

Climate change remains a topic to be investigated with implications on occupant comfort and energy conservation. All previously mentioned future research opportunities can be explored with the consideration of climatic dynamics as an important variable in the process. This is important for the framework to be stable and able to offer both objectives. However, this area limits its researchers to only reactive approaches instead of proactive, because climate and weather are uncontrollable. By understanding climate and accommodate for it, designs can acquire a robust energy-efficient position [81].

4.3. Objectives of the literature review

This review explores and categorises the existing literature of energy consumption in buildings considering occupant comfort. The suggested categorisation includes occupant behaviour, building envelope, and building energy systems. The discussion presented in Section 3 fulfills the first objective of this study. In addition, the second objective of identification of challenges in achieving comfortable and energy- efficient buildings is presented in Sections 3 and 4, including climate, occupancy detection, and comfort models. Finally, the review recommends future research opportunities in the discipline to help overcome challenges obtained from second objective.

5. Conclusion

Designing energy-efficient buildings considering only economic impacts does not necessarily lead to comfortable indoor environments for occupants. In fact, energy conservation measures (ECMs) have potential to accomplish both cost savings and occupants’ comfort if they are optimised for these objectives. This review has presented recent advancements in the field of building energy conservation with consideration of trends, modelling approaches, and challenges. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method was used to identify the related literature and avoid bias. Three main elements are found to be the main players in the process of buildings energy consumption, namely, occupant behaviour (OB), building envelope, and building energy systems (BES). OB is an evolving stream of research with room for improvement because current practice is mostly deterministic and does not reflect the real stochastic nature of OB. In addition, occupant behaviour detection techniques raise concerns about occupants’ privacy, and create a challenge of establishing Table 1 (continued)

Source Occupant

Behaviour Envelope Building Energy Systems

Approach/

technique Journal

[36] . x x Literature

[80] . x . Simulation/

comparison Energy and Buildings

[81] . x x multi-stage

simulation/

comparison

[82] . x . optimisation/

sensitivity analysis

Journal of Cleaner Production

[52] . x . Multi-

Applied Energy

[42] . x . Experimental/

simulation Journal of Building Engineering

[63] . . x BES schedule

optimisation Energy and Buildings

[57] . . x Innovative

BES configuration

Solar Energy

[26] x . . Experimental/

simulation Energy and Buildings

[25] x x . Experimental/

simulation/

comparison

[83] . x . Optimisation/

validation Applied Energy

[84] x x . Simulation Building and

Environment

[48] x x . optimisation/

evaluation Energy and Buildings

[38] . x . Simulation Solar Energy

[46] . x . ANN Energy

[9] x . . ABM Energy and

Buildings

[85] x . . ANN Energy and

Buildings

(9)

behavioural models that are only suitable for building controls. In contrast, building envelope research domain is an established field and relies on design of robust and resilient envelopes to accommodate changes caused by real-world uncertainties. This is attributed to the dynamics that create unique sets of critical parameters in building envelopes based on climatic location and types of occupants. A significant area of current research is found to be automated controls inherently designed to draw on occupant behaviour data, such as Data Predictive Control (DPC) which provides dynamic trade-offs between the two objectives of occupant comfort and energy conservation. This trend of research stream shows that current comfort assessment tools such as Predicted Mean Vote (PMV) are outdated techniques that do not reflect the subjective feeling of comfort among different occupants. In addition, findings of this review show that several challenges exist to improve effectiveness of ECMs. Literature emphasises the importance of understanding existing challenges, such as climate, occupancy detection, and comfort models. Incorporating these in the design warrants the achievement of energy-efficient, comfortable buildings. Finally, this study recommends opportunities for future research, including personalised comfort models, building energy storage systems, and critical parameters of building envelopes in different climates. This review is expected to assist researchers in acquiring an overview of the existing body of knowledge on the building energy conservation domain and interactions with occupant comfort and energy efficiency.

Declaration of competing interest

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Therefore, the authors wish to confirm that there are no known conflicts of interest associated with this publication.

References

[1] X. Cao, X. Dai, J. Liu, Building energy-consumption status worldwide and the state- of-the-art technologies for zero-energy buildings during the past decade, Energy Build. 128 (2016) 198–213.

[2] M.W. Ahmad, et al., Building energy metering and environmental monitoring – a state-of-the-art review and directions for future research, Energy Build. 120 (2016) 85–102.

[3] W.H. Correale, Energy conservation IN modern office buildings, J. Environ. Syst. 3 (4) (1973) 307–316.

[4] F. Madonna, et al., Influence of comfort expectations on building energy need, Energy Procedia 140 (2017) 265–276.

[5] M.U. Mehmood, et al., A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment, Energy Build. 202 (2019) 109383.

[6] M.S. Andargie, M. Touchie, W. O’Brien, A review of factors affecting occupant comfort in multi-unit residential buildings, Build. Environ. 160 (2019) 106182.

[7] D. Moher, et al., Preferred reporting items for systematic reviews and meta- analyses: the PRISMA statement, Ann. Intern. Med. 151 (4) (2009) 264–269.

[8] B.F. Balvedi, E. Ghisi, R. Lamberts, A review of occupant behaviour in residential buildings, Energy Build. 174 (2018) 495–505.

[9] Y.S. Lee, A.M. Malkawi, Simulating multiple occupant behaviors in buildings: an agent-based modeling approach, Energy Build. 69 (2014) 407–416.

[10] T. Parkinson, A. Parkinson, R. de Dear, Continuous IEQ monitoring system: context and development, Build. Environ. 149 (2019) 15–25.

[11] R.-L. Hwang, et al., Thermal perceptions, general adaptation methods and occupant’s idea about the trade-off between thermal comfort and energy saving in hot–humid regions, Build. Environ. 44 (6) (2009) 1128–1134.

[12] E. Delzendeh, et al., The impact of occupants’ behaviours on building energy analysis: a research review, Renew. Sustain. Energy Rev. 80 (2017) 1061–1071.

[13] A. Francisco, et al., Occupant perceptions of building information model-based energy visualizations in eco-feedback systems, Appl. Energy 221 (2018) 220–228.

[14] M. Ashouri, et al., Development of a ranking procedure for energy performance evaluation of buildings based on occupant behavior, Energy Build. 183 (2019) 659–671.

[15] S. Chen, et al., Definition of occupant behavior in residential buildings and its application to behavior analysis in case studies, Energy Build. 104 (2015) 1–13.

[16] Z. Wang, et al., Inferring occupant counts from Wi-Fi data in buildings through machine learning, Build. Environ. 158 (2019) 281–294.

[17] H.N. Rafsanjani, A. Ghahramani, Towards utilizing internet of things (IoT) devices for understanding individual occupants’ energy usage of personal and shared appliances in office buildings, Journal of Building Engineering 27 (2020).

[18] J. Yao, Modelling and simulating occupant behaviour on air conditioning in residential buildings, Energy Build. 175 (2018) 1–10.

Fig. 6. Challenges and opportunities conceptual relationship.

(10)

[19] C. Berger, A. Mahdavi, Review of current trends in agent-based modeling of building occupants for energy and indoor-environmental performance analysis, Build. Environ. 173 (2020) 106726.

[20] Z. Ding, et al., Agent-based model for simulating building energy management in student residences, Energy Build. 198 (2019) 11–27.

[21] W. Wang, et al., Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification, Appl. Energy 236 (2019) 55–69.

[22] J. Kim, S. Schiavon, G. Brager, Personal comfort models – a new paradigm in thermal comfort for occupant-centric environmental control, Build. Environ. 132 (2018) 114–124.

[23] H. Yoshino, T. Hong, N. Nord, IEA EBC annex 53: total energy use in buildings—analysis and evaluation methods, Energy Build. 152 (2017) 124–136.

[24] T. Hong, et al., Advances in research and applications of energy-related occupant behavior in buildings, Energy Build. 116 (2016) 694–702.

[25] F. Yousefi, Y. Gholipour, W. Yan, A study of the impact of occupant behaviors on energy performance of building envelopes using occupants’ data, Energy Build.

148 (2017) 182–198.

[26] P. Lundqvist, M. Risberg, L. Westerlund, The importance of adjusting the heating system after an energy-retrofit of buildings in a sub-Arctic climate, Energy Build.

(2020) 217.

[27] A. Ghahramani, K. Dutta, B. Becerik-Gerber, Energy trade off analysis of optimized daily temperature setpoints, Journal of Building Engineering 19 (2018) 584–591.

[28] L. La Fleur, P. Rohdin, B. Moshfegh, Energy use and perceived indoor environment in a Swedish multifamily building before and after major renovation, Sustainability 10 (2018) 766, 766, 2018. 10.

[29] B. Raji, M.J. Tenpierik, A. van den Dobbelsteen, An assessment of energy-saving solutions for the envelope design of high-rise buildings in temperate climates: a case study in The Netherlands, Energy Build. 124 (2016) 210–221.

[30] R. Pacheco, J. Ordo´nez, G. Martínez, Energy efficient design of building: a review, ˜ Renew. Sustain. Energy Rev. 16 (6) (2012) 3559–3573.

[31] S.B. Sadineni, S. Madala, R.F. Boehm, Passive building energy savings: a review of building envelope components, Renew. Sustain. Energy Rev. 15 (8) (2011) 3617–3631.

[32] P.K. Latha, Y. Darshana, V. Venugopal, Role of building material in thermal comfort in tropical climates - a review, Journal of Building Engineering 3 (2015) 104–113.

[33] A.V. S´a, et al., Thermal enhancement of plastering mortars with Phase Change Materials: experimental and numerical approach, Energy Build. 49 (2012) 16–27.

[34] X. Jin, M.A. Medina, X. Zhang, On the placement of a phase change material thermal shield within the cavity of buildings walls for heat transfer rate reduction, Energy 73 (2014) 780–786.

[35] M. Vautherot, F. Mar´echal, M.M. Farid, Analysis of energy requirements versus comfort levels for the integration of phase change materials in buildings, Journal of Building Engineering 1 (2015) 53–62.

[36] M. Song, et al., Review on building energy performance improvement using phase change materials, Energy Build. 158 (2018) 776–793.

[37] Y. Sang, et al., Experimental investigation and EnergyPlus-based model prediction of thermal behavior of building containing phase change material, Journal of Building Engineering 12 (2017) 259–266.

[38] J. Hu, X. Yu, Thermo and light-responsive building envelope: energy analysis under different climate conditions, Sol. Energy 193 (2019) 866–877.

[39] D.K. Bhamare, M.K. Rathod, J. Banerjee, Numerical model for evaluating thermal performance of residential building roof integrated with inclined phase change material (PCM) layer, Journal of Building Engineering 28 (2020).

[40] H. Huang, et al., Solvothermal synthesis of Sb: SnO2 nanoparticles and IR shielding coating for smart window, Mater. Des. 88 (2015) 384–389.

[41] C. Marino, A. Nucara, M. Pietrafesa, Does window-to-wall ratio have a significant effect on the energy consumption of buildings? A parametric analysis in Italian climate conditions, Journal of Building Engineering 13 (2017) 169–183.

[42] G. Kirankumar, et al., Thermal and cost analysis of various air filled double glazed reflective windows for energy efficient buildings, Journal of Building Engineering 28 (2020).

[43] G. Franchini, G. Brumana, A. Perdichizzi, Monitored performance of the first energy+autonomous building in Dubai, Energy Build. (2019) 205.

[44] C. Waibel, R. Evins, J. Carmeliet, Co-simulation and optimization of building geometry and multi-energy systems: interdependencies in energy supply, energy demand and solar potentials, Appl. Energy 242 (2019) 1661–1682.

[45] M.H. Wu, T.S. Ng, M.R. Skitmore, Sustainable building envelope design by considering energy cost and occupant satisfaction, Energy for Sustainable Development 31 (2016) 118–129.

[46] D.K. Bui, et al., An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings, Energy 190 (2020).

[47] A.-T. Nguyen, S. Reiter, P. Rigo, A review on simulation-based optimization methods applied to building performance analysis, Appl. Energy 113 (2014) 1043–1058.

[48] S. Lu, J. Li, B. Lin, Reliability analysis of an energy-based form optimization of office buildings under uncertainties in envelope and occupant parameters, Energy Build. (2020) 209.

[49] T. Hong, J. Kim, M. Lee, A multi-objective optimization model for determining the building design and occupant behaviors based on energy, economic, and environmental performance, Energy 174 (2019) 823–834.

[50] A. Kirimtat, et al., Multi-objective energy and daylight optimization of amorphous shading devices in buildings, Sol. Energy 185 (2019) 100–111.

[51] H. Sghiouri, et al., Shading devices optimization to enhance thermal comfort and energy performance of a residential building in Morocco, Journal of Building Engineering 18 (2018) 292–302.

[52] M. Leˇsnik, et al., Optimal design of timber-glass upgrade modules for vertical building extension from the viewpoints of energy efficiency and visual comfort, Appl. Energy (2020) 270.

[53] R. Wu, et al., Multiobjective optimisation of energy systems and building envelope retrofit in a residential community, Appl. Energy 190 (2017) 634–649.

[54] H. Li, S. Wang, H. Cheung, Sensitivity analysis of design parameters and optimal design for zero/low energy buildings in subtropical regions, Appl. Energy 228 (2018) 1280–1291.

[55] H. Li, S. Wang, R. Tang, Robust optimal design of zero/low energy buildings considering uncertainties and the impacts of objective functions, Appl. Energy (2019) 254.

[56] V.S.K.V. Harish, A. Kumar, A review on modeling and simulation of building energy systems, Renew. Sustain. Energy Rev. 56 (2016) 1272–1292.

[57] R. Stropnik, et al., Improved thermal energy storage for nearly zero energy buildings with PCM integration, Sol. Energy 190 (2019) 420–426.

[58] H.J. Han, et al., An advanced lighting system combining solar and an artificial light source for constant illumination and energy saving in buildings, Energy Build.

(2019) 203.

[59] M. Wang, et al., Comparison of energy performance between PV double skin facades and PV insulating glass units, Appl. Energy 194 (2017) 148–160.

[60] L. Atzori, A. Iera, G. Morabito, The internet of things: a survey, Comput. Network.

54 (15) (2010) 2787–2805.

[61] J.P. Roselyn, et al., Development and implementation of novel sensor fusion algorithm for occupancy detection and automation in energy efficient buildings, Sustainable Cities and Society 44 (2019) 85–98.

[62] E. Png, et al., An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings, Appl. Energy 239 (2019) 408–424.

[63] A. Pallante, et al., Decision support methodologies and day-ahead optimization for smart building energy management in a dynamic pricing scenario, Energy Build.

(2020) 216.

[64] M. Bati´c, et al., Combined energy hub optimisation and demand side management for buildings, Energy Build. 127 (2016) 229–241.

[65] F. Ascione, et al., Multi-objective optimization of the renewable energy mix for a building, Appl. Therm. Eng. 101 (2016) 612–621.

[66] H. Pandˇzi´c, Optimal battery energy storage investment in buildings, Energy Build.

175 (2018) 189–198.

[67] H. Satyavada, S. Baldi, An integrated control-oriented modelling for HVAC performance benchmarking, Journal of Building Engineering 6 (2016) 262–273.

[68] F. Bünning, et al., Experimental demonstration of data predictive control for energy optimization and thermal comfort in buildings, Energy Build. (2020) 211.

[69] M.H. Hasan, F. Alsaleem, M. Rafaie, Sensitivity study for the PMV thermal comfort model and the use of wearable devices biometric data for metabolic rate estimation, Build. Environ. 110 (2016) 173–183.

[70] S. Hussain, et al., Comfort-based fuzzy control optimization for energy conservation in HVAC systems, Contr. Eng. Pract. 32 (2014) 172–182.

[71] A. Ashouri, et al., Sensitivity analysis for robust design of building energy systems, Energy 76 (2014) 264–275.

[72] C. Wang, et al., Robust scheduling of building energy system under uncertainty, Appl. Energy 167 (2016) 366–376.

[73] F. Ascione, et al., Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort, Energy Build.

111 (2016) 131–144.

[74] M. Killian, M. Kozek, Ten questions concerning model predictive control for energy efficient buildings, Build. Environ. 105 (2016) 403–412.

[75] J. Ge, et al., Energy efficiency optimization strategies for university research buildings with hot summer and cold winter climate of China based on the adaptive thermal comfort, Journal of Building Engineering 18 (2018) 321–330.

[76] T. Cheung, et al., Analysis of the accuracy on PMV – PPD model using the ASHRAE global thermal comfort database II, Build. Environ. 153 (2019) 205–217.

[77] P. Block, et al., NEST HiLo: investigating lightweight construction and adaptive energy systems, Journal of Building Engineering 12 (2017) 332–341.

[78] A. Schieweck, et al., Smart homes and the control of indoor air quality, Renew.

Sustain. Energy Rev. 94 (2018) 705–718.

[79] Y. Geng, et al., A review of operating performance in green buildings: energy use, indoor environmental quality and occupant satisfaction, Energy Build. 183 (2019) 500–514.

[80] M. Li, et al., Different responses of cooling energy consumption in office buildings to climatic change in major climate zones of China, Energy Build. 173 (2018) 38–44.

[81] N. Jung, et al., Energy performance analysis of an office building in three climate zones, Energy Build. 158 (2018) 1023–1035.

[82] Q. Tushar, et al., Optimizing the energy consumption in a residential building at different climate zones: towards sustainable decision making, J. Clean. Prod. 233 (2019) 634–649.

[83] Y.H. Lin, et al., Design optimization of office building envelope configurations for energy conservation, Appl. Energy 171 (2016) 336–346.

[84] T. Buso, et al., Occupant behaviour and robustness of building design, Build.

Environ. 94 (2015) 694–703.

[85] Z. Deng, Q. Chen, Simulating the impact of occupant behavior on energy use of HVAC systems by implementing a behavioral artificial neural network model, Energy Build. 198 (2019) 216–227.

(11)

[86] W. Pan, K. Li, Y. Teng, Rethinking system boundaries of the life cycle carbon emissions of buildings, Renew. Sustain. Energy Rev. 90 (2018) 379–390.

[87] P.X.W. Zou, et al., A mixed methods design for building occupants’ energy behavior research, Energy Build. 166 (2018) 239–249.

[88] F. Ascione, et al., A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin, Appl. Energy 241 (2019) 331–361.

[89] M.A. Humphreys, J.F. Nicol, I.A. Raja, Field studies of indoor thermal comfort and the progress of the adaptive approach, Adv. Build. Energy Res. 1 (1) (2007) 55–88.

[90] K.H. Cheong, et al., A simulation-aided approach in improving thermal-visual comfort and power efficiency in buildings, Journal of Building Engineering 27 (2020).