Whole-building Energy Research Articles

Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements

This study evaluates the energy performance of ASHRAE Guideline 36–compliant control (ASHRAE 36 control) and reinforcement learning (RL)–based control through experimental field tests and a simulation study. Three field tests were conducted at Oak Ridge National Laboratory’s commercial building test facility in Oak Ridge, Tennessee: a baseline with a baseline conventional control, a test with ASHRAE 36 control, and a test with RL-based control. The selected ASHRAE 36 controls were trim and respond control, as well as variable air volume (VAV) box control. We compared the measured supply air temperature of the rooftop unit, VAV box supply air temperature, and VAV box supply airflow rate across the three test cases. The field data indicated that ASHRAE 36 controls operated as specified by ASHRAE Guideline 36. Based on these data, ASHRAE 36 control achieved a 45 % reduction in hourly averaged HVAC energy consumption compared with the baseline, and RL-based control achieved a 66 % reduction. These potential annual energy savings were confirmed using a calibrated whole-building energy model. Compared with the baseline, ASHRAE 36 control reduced HVAC energy consumption by 42 %, and RL-based control achieved a 54 % reduction. Furthermore, RL-based control reduced total HVAC energy consumption by 21 % more than ASHRAE 36 control.

Self-powered electrochromic smart window helps net-zero energy buildings

Net-zero energy buildings are becoming an important energy-saving and low-carbon technology for global environmental protection and resource conservation. As the least energy-efficient building components, windows have promoted the development of electrochemically light/thermal management, where smart windows without external electrical supplies are highly desired for net-zero energy buildings. Here, we develop a self-powered electrochromic smart window system to regulate solar radiation transmittance, constructed by a triboelectric nanogenerator (TENG) and a high-performance electrochromic device. The visible–near-infrared (VIS-NIR) dual-band electrochromic device is well-designed based on an eco-friendly low-energy demand phenothiazine redox ionic liquid, demonstrating a fast switching response of ﹤1.9 s, a large transmittance modulation of >51.8 %, and outstanding electrochemical durability (5.5 % attenuation after 4000 cycles). Upon continuous pressing/releasing motions, the integrated TENG-powered electrochromic smart window (TECSW) displays a prompt light/thermal management behavior with desirable privacy preservation performance (transmittance modulation of 51 % in the visible region) and excellent thermal management property (12.3 °C reducti°n). The whole-building energy simulations show that TECSW can save 347.79 MJ/m2yr (amounting to 96.60 kWh/m2yr) of operational Heating Ventilation Air Conditioning (HVAC) warming/cooling energy consumption among 34 cities in China, accounting for 17.25 % of the total energy consumption. This research provides new insight into designing green and energy-efficient smart windows for net-zero energy buildings.

A novel approach based on equivalent sky radiative temperature for quick computation of radiative cooling in building energy simulation

Passive radiative cooling materials (RCM) can cool surfaces by radiating heat to outer space via long-wave radiation. In simulation application, co-simulation of the thermal process between RCMs and building structures is generally required; however, the high computing time cost and low integration level have hindered the integrated whole-building simulation in architectural design. Traditional building energy simulations utilize the effective sky temperature to numerically approximate the effect of sky radiation. However, given the limitations of effective sky temperature concept, it is more suitable to evaluate diffusive gray surfaces while not fully capturing the characteristics of radiative heat transfer for spectrum-selective surfaces. In this study, an innovative equivalent-sky-radiative temperature was introduced as a new equivalent parameter to accurately simulate the thermal performance of RCMs in buildings. Embedding this new parameter into whole-building energy simulation tools such as EnergyPlus and DeST is expected to significantly improve the calculation accuracy and reduce the computational cost. Case studies in this work have shown an average error of 5.47 W/m2. The generation of the new parameter for 50 cities worldwide also demonstrated the technical feasibility of applying the equivalent-sky-radiative temperatures to evaluate the thermal performance and energy savings of real-world RCMs in buildings.

Multi-criteria decision support framework for climate change-sensitive thermal comfort evaluation in European buildings

This paper proposes a novel multi-criteria decision support framework for climate change-sensitive evaluation of building thermal comfort performance in European buildings. The proposed framework considers various comfort categories based on building use and condition, comfort models based on building operation, sensitivity to climate change, key performance indicators, and their thresholds. The proposed framework is then implemented using a passive house-certified high-performance timber dwelling near Brussels as a case study. The thermal comfort in the free-running reference timber dwelling is assessed using a whole-building energy performance simulation model with a static threshold of 26°C for the bedrooms and an adaptive threshold for the other occupied zones. The analysis found an increase in indoor overheating degree by 1.7°C and ambient warmness degree by 6.5°C during heat waves from the current scenario to the end of the century scenario. However, the reference timber dwelling effectively suppressed the impacts of climate change with varying degrees of success towards the end of the century. The proposed framework is intended to support decision-makers in effectively evaluating performance during the early stages of building design. The study highlights the need for further research on building performance assessment techniques and guidelines in a changing climate.

Techno-economic analysis to determine the potential of perovskite-based PV blinds for buildings

This paper analyses a novel perovskite-based Photovoltaic (PV) blind system for buildings. Some buildings are unable to install traditional solar systems because they cannot install PV panels on the roof or facade. Traditional PV materials have a long value chain, also the resulting solar panels are bulky and heavy and not suitable for blind applications. Recently, Perovskite crystals, a new semiconductor material, have shown potential for reducing the manufacturing cost while providing high energy conversion efficiency. However, a significant issue with PV blind systems is that they typically receive less solar radiation than traditional rooftop PV systems, resulting in less electricity generation, particularly when the PV blinds are used indoors behind the windows. In addition, unlike conventional rooftop PV panels, PV blind systems affect the total energy consumption in the building by changing the heating and cooling demands as well as interior lighting. Utilizing these new PV blind systems effectively necessitates a holistic analysis of solar potential, performance, and cost-effectiveness. In this study, whole-building energy simulation, techno-economical modeling, and parametric analysis will be used to compare perovskites PV blinds and conventional rooftop PV arrays.

Impacts of lighting and plug load variations on residential building energy consumption targeting zero energy building goals

Mitigating building energy use and carbon emissions in the buildings sector is a globally recognized goal. This is being pursued through the adoption of zero-energy building (ZEB) targets, with formal government regulations and policies actively embraced in domestic marketplaces. However, building assessment methods used for the ZEB certification system in South Korea have garnered substantial critique from building energy engineers and practitioners for their practical applicability. This study investigated energy consumption patterns of single-family residential buildings, focusing on two major energy end-use categories: lighting- and plug-loads. Empirical analyses and field measurement data were used to construct representative profiles of lighting and plug loads, as well as power density values, based on data from existing residential buildings. These distinct profiles of lighting and plug loads were used as input data for a residential ZEB evaluation. The measurement data from 60 detached houses in South Korea was utilized to derive four representative profiles for the category's loads. These profiles were subsequently employed for simulation-based analyses to assess load diversity. A whole-building energy simulation model was developed to conduct annual building energy analyses and assess ZEB certification based on the set and applied load profiles. Results showed that building energy consumption could vary by up to 23% based on different lighting load patterns and by as much as 58% for differing plug load patterns. These findings underline the importance of refining the current domestic ZEB certification process by considering the influence of diverse domestic load profiles on energy consumption and building certification grades.

Investigation on Architecture and Energy End-Use Characteristics of Korean Apartments for Whole-Building Energy Simulation Modeling

Investigation on Architecture and Energy End-Use Characteristics of Korean Apartments for Whole-Building Energy Simulation Modeling

Advances in the Co-Simulation of Detailed Electrical and Whole-Building Energy Performance

This article describes recent co-simulation advances for the simultaneous modeling of detailed building electrical distribution systems and whole-building energy performance. The co-simulation architecture combines the EnergyPlus® engine for whole-building energy modeling with a new Modelica library for building an electrical distribution system model that is based on harmonic power flow. This new library allows for a higher-fidelity modeling of electrical power flows and losses within buildings than is available with current building electrical modeling software. We demonstrate the feasibility of the architecture by modeling a simple, two-zone thermal chamber with internal power electronics converters and resistive loads, and we validate the model using experimental data. The proposed co-simulation capability significantly expands the capabilities of building electrical distribution system models in the context of whole-building energy modeling, thus enabling more complex analyses than would have been possible with individual building performance simulation tools that are used to date.

Performance assessment of active insulation systems in residential buildings for energy savings and peak demand reduction

Active insulation systems (AISs) in buildings are envelopes that integrate thermal insulation, thermal energy storage, and controls. Although different designs for AISs have been proposed in the literature, a comprehensive analysis of feasible AISs is lacking. This paper discusses the energy performance, peak demand reduction potential, and performance characteristics of an AIS that uses a concrete wall as thermal mass sandwiched between two solid-state thermal switches (STSs). These STSs change their thermal conductivity using an on/off metal switch to create or break a thermal bridge across the STS. This paper first describes the experimental setup, used to determine the ratio of thermal resistance during R-high (low thermal conductivity) and R-low (high thermal conductivity) states of the STSs. This ratio was then used in whole-building energy simulations to evaluate the performance of AIS walls across different climate zones with/without a freeze timer of 60 min. The timer was added to reduce the number of switches of STSs from one state to another, and hence the energy needed for these switches. Analysis of the switching frequency and interval of STSs, thermal conductivity of walls, impact of wall orientation, and heat transfer through the wall from the use of AIS at different climate zones/locations were performed. The simulation results show that the AIS can achieve energy savings ranging from ∼ 980 to 2,290 kWh in a single-family home with a floor area of ∼ 220 m2 compared with an IECC 2018 baseline. The energy savings was higher in dry climate zones which represent 17% of residential buildings in the United States, compared to humid or marine climate.

A whole-building data-driven fault detection and diagnosis approach for public buildings in hot climate regions

Fault detection and diagnosis (FDD) approaches comprise three main pillars: model-based, knowledge-based, and data-driven strategies. Data-driven approaches prioritise operational data and do not necessitate in-depth understanding of the system's background; yet, significant amounts of data is required, which often poses challenges to researchers. Since simulated data is inexpensive and can run numerous faults types with varying severities and time periods, it has been used in data-driven FDD analysis. However, the majority of FDD approaches are implemented at the system level of buildings. However, most buildings have numerous systems with distinct features. Furthermore, using individualised system-level analysis makes it difficult to see system-to-system relationships. Currently, there is a significant underrepresentation of research that investigate the applications of FDD models under whole-building scenarios, so as to identify a wider range of energy consumption related faults in buildings. Furthermore, since data-driven approaches significantly depend on the quantities of training data, it becomes challenging to diagnose faults that have limited features. As a result, this study diagnoses numerous building systems faults, including single and simultaneous faults with limited features. This is implemented within the context of the whole-building energy performance of religious buildings in hot climatic areas, employing data-driven FDD methodologies. Various multi-class classification approaches were investigated to classify both the normal condition and faulty classes. Furthermore, feature extraction methodologies were compared to quantify their potential for improving the diagnosis. In addition to the classification evaluation metrics, one-way ANOVA and Tukey-Kramer tests were implemented to examine the significance of the reported performance differences. RF classifier obtained highest classification accuracy during validation and testing with about 90%, indicating a promising performance in whole-building faults analysis. The adoption of feature extraction techniques did not improve classification performance, thereby emphasising that some classifiers may perform better with high-dimensional datasets.

Evaluation of Multi-Functional Variable Refrigerant Flow System With Thermal Energy Storage and Photovoltaic-Based Distributed System for Net-Zero Energy Home Design

Abstract Efficient heating and cooling systems and renewable energy sources are crucial for effectively designing net-zero energy homes (NZEHs). The study proposes using a multi-functional variable refrigerant flow system with hydraulic heat recovery (MFVRF-H2R) to reduce heating, ventilating, and air-conditioning (HVAC) and hot water energy usage, offering a practical approach to enable NZEH solutions. Photovoltaic (PV)-based on-site power generation is utilized to achieve zero energy performance in residential buildings. A building energy simulation study is conducted to assess the effectiveness of the combined systems in various climate conditions. To develop the simulation model, the US National Institute of Standards and Technology (NIST)’s net-zero energy residential test facility is used as the benchmark for NZEH baseline models. The MFVRF-H2R system is incorporated into the NZEH baseline to propose a more-energy-efficient design with heat recovery technology. eQUEST and post-processing calculations are used to simulate NZEH performance, comparing whole-building energy end-use and PV capacity for the baseline and alternative models with MFVRF-H2R. Results demonstrate that the proposed variable refrigerant flow (VRF)-based NZEH design can provide potential energy savings of up to 32% for cooling energy under various climate zones. Moreover, the NZEH design with the proposed MFVRF-H2R can achieve up to a 90% reduction in domestic hot water usage compared to an NZEH design without VRF heat recovery technology. The study suggests that the MFVRF-H2R system can provide practical and realistic solutions for making HVAC energy-efficient by minimizing thermal waste and reusing it for other thermal parts of the building, such as hot water applications. Consequently, this study highlights the effectiveness of the MFVRF-H2R system in designing NZEHs while considering heat recovery and renewable energy technologies.

Dynamic Thermal Performance Analysis of PCM Products Used for Energy Efficiency and Internal Climate Control in Buildings

PCMs are attractive for the future generation of buildings, where energy efficiency targets and thermal comfort expectations are increasingly prioritized. Experimental analysis of local thermal processes in these dynamic components and whole-building energy consumption predictions are essential for the proper implementation of PCMs in buildings. This paper discusses the experimental analysis of the thermophysical characteristics of both a latent heat storage material (PCM) and a product containing this PCM. The prototype product under investigation is a panelized PCM technology containing inorganic, salt-hydrate-based PCM. The thermal analysis includes studies of melting and freezing temperatures, enthalpy changes during phase change processes, nucleation intensity, sub-cooling effects, and PCM stability. The PCM’s stability is also investigated, as is the ability of PCM products to control local temperatures and peak load transmission times. Two inorganic PCM formulations based on calcium chloride hexahydrate (CaCl2.6H2O) were prepared and tested in laboratory conditions. Material-scale testing results were compared with outcomes from the system-scale analysis, using both laboratory test methods as well as field exposure in test huts. This work demonstrates that PCM technologies used in buildings can effectively control both the magnitude of thermal storage capacity as well as the time of the peak thermal load. It was found that commonly used material-scale testing methods may not always be beneficial in assessing the dynamic thermal performance characteristics of building technologies containing PCMs.

Resilience and sustainability-informed probabilistic multi-criteria decision-making framework for design solutions selection

In this paper, a novel probabilistic multi-criteria decision-making (MCDM) framework for sustainable and resilient building design solutions selection is developed to optimize alternatives or rehabilitation strategies in a life cycle context. The proposed framework holistically takes into account the buildings' resilience metrics, environmental sustainability, and energy consumption. The life cycle economic cost, global warming potential (GWP), primary energy use (PEU), and social impact are used to represent the buildings’ resilience and sustainability. The second-generation Performance-based Earthquake Engineering procedure and FEMA P-58 method for time-based seismic performance analyses are used to evaluate earthquake-induced impact. The whole-building energy modeling (BEM) is adopted to assess energy use during the operational phase. In addition, life cycle analysis (LCA) and life cycle cost analysis (LCCA) are carried out to assess the life cycle performance of different design alternatives regarding environmental impacts and cost, respectively. Aiming to consider the various uncertainties in the decision-making process, a novel MCS-MCDM method is developed by combining the Monte Carlo simulation (MCS) with the traditional MCDM method. The proposed framework is applied to analyze hypothetical cases considering different geographic locations, structural systems, and building envelope systems, and recommendations are given according to the analysis results. The case study demonstrates the advantages of the proposed framework over traditional frameworks and its guidance for architectural design decisions.

Origins of whole-building energy simulations for high-performance commercial buildings: Contributions of NATEOUS, SHEP, TACS, CP-26, and RESPTK programs

Previous studies have traced the earliest origins of today’s whole-building energy simulation programs, such as EnergyPlus, eQUEST, DOE-2.1E, TRNSYS, TRACE, and HAP, to two public simulation programs in the early 1970’s, including: the Post Office program and the NBSLD program. However, in the 1960’s, there were earlier public simulation programs that had a significant influence on developments of the Post Office and NBSLD programs and are therefore related to many of the today’s whole-building energy simulation programs but were not discussed in previous studies, including: the NATEOUS program, the SHEP program, and the TACS program. In addition, there were public supplementary calculation programs that also contributed to developments of the Post Office and NBSLD programs, including: the CP-26 program and the RESPTK program. Therefore, the purpose of this study is to further clarify the earliest origins of today’s whole-building energy simulation programs based on the analysis of these early computer programs. This study extends the previous research (Oh and Haberl 2016) that discussed the origins of today’s whole-building energy simulation programs for high-performance commercial buildings and the thermal analysis methods used in these programs.

Degradation of Insulating Glass Units: Thermal Performance, Measurements and Energy Impacts

Insulating glass unit (IGU) degradation has been studied extensively. However, there is limited understanding of how present durability evaluation standards relate to product lifetime. Furthermore, there is debate on how to quantify performance of installed windows over time to better understand degradation processes. More knowledge on these topics is required to link durability evaluation to product lifetime projections based on energy performance. Energy models provide helpful estimations of total annual building energy consumption. However, most models are based on “as installed” performance of envelope components and fail to account for performance degradation. This can lead to an underestimation of building lifetime energy consumption. A better understanding of the relationship between durability and energy performance can inform integration of degradation dynamics into energy modeling software. This will improve lifetime building energy consumption estimations as well as inform appropriate retrofit strategies and timing. This paper reviews current durability literature, various standards for window performance ratings and weathering methods, existing in situ IGU energy performance measurement techniques, and whole-building energy effects. The challenges and disparities among various studies are analyzed and discussed. The authors hope that further work in this area will lead to the development of improved in situ test methods to assess IGU degradation in the field and link this knowledge to improved energy performance modeling approaches.

Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting

Building energy consumption accounts for about 40% of global primary energy use and 30% of worldwide greenhouse gas (GHG) emissions. Among the energy-related factors present in buildings, heating, cooling, and air-conditioning (HVAC) systems are considered major contributors to whole-building energy use. To improve the energy efficiency of HVAC systems and mitigate whole-building energy consumption, accurately predicting the building energy consumption can play a significant role. Although many prediction approaches are available for building energy use, a machine learning-based modeling approach (i.e., black box models) has recently been considered to be one of the most promising building energy modeling techniques due to its simplicity and flexibility compared to physics-based modeling techniques (i.e., white box models). This study presents a building energy load forecasting method based on long-term short-term memory (LSTM) and transfer learning (TL) strategies. To implement this approach, this study first conducted raw data pre-processing analysis to generate input datasets. A hospital building type was considered for a case study in the first stage. The hospital prototype building model, developed by the U.S. department of energy (DOE), was used to generate an initial input training and testing dataset for source domain tasks before the transfer learning process. For the transfer learning process in a target domain, a simulation-based analysis was also conducted to obtain target datasets by assuming limited data lengths in different weather conditions. The training and testing procedures were performed using separate cooling and heating periods with and without the transfer learning process for source and target domain tasks, respectively. Lastly, a comparative analysis was carried out to investigate how the accuracy of LSTM prediction can be enhanced with the help of transfer learning strategies. The results from this study show that the developed LSTM-TL model can achieve better performance than the prediction model, which only uses LSTM under different weather conditions. In addition, accurate performance can vary according to different transfer learning methods with frozen and fine-tuning layers and locations.

Early design evaluation of low-rise school building morphology on energy performance: Climatic contexts of Southeast Europe

Contemporary societies have shifted their focus towards sustainable and resilient public buildings, transcending the institutional and functional role of the physical structures. School buildings remain the second highest expenditure of municipalities' total running costs. The impact on well-being and improved knowledge, and the average lifespan of school buildings, imply the necessity to pay special attention to its expected performance from the early design phase.The present research aims to estimate the impact of early design evaluation of open-school building morphology on energy performance in four climatic contexts of Southeast Europe. Design variables, including building shape and orientation, are selected for a comprehensive whole-building energy performance analysis. The results highlight the method's efficiency in reducing a maximum of 35.4% of the annual energy demand and increasing a maximum of 1.21 °C the thermal comfort in classrooms through geometry optimization. Suggestions are made on the appropriateness level of each typology for each studied climatic context. A fundamental framework for early design decision-making stages is generated through the performed simulation results.

Experimental data for validation of a naturally ventilated single-story Double Skin Facade in whole-building energy simulation tools

Experimental data for validation of a naturally ventilated single-story Double Skin Facade in whole-building energy simulation tools

Models for validation of a naturally ventilated single-story Double Skin Facade in whole-building energy simulation tools

Models for validation of a naturally ventilated single-story Double Skin Facade in whole-building energy simulation tools

Modelling double skin façades (DSFs) in whole-building energy simulation tools: Validation and inter-software comparison of naturally ventilated single-story DSFs

Building energy simulation (BES) tools offer the possibility to integrate double skin façade (DSF) technologies into whole building simulation through dedicated modules or possible workarounds. However, the reliability of such tools in predicting the dynamic heat and mass transfer processes within the DSFs is still to be determined. Therefore, this paper aims to assess the performance of four popular BES tools (i.e. EnergyPlus, TRNSYS, IDA-ICE and IES-VE) in predicting the thermal behaviour of one-storey naturally ventilated DSF in three different ventilation modes. To evaluate their capability to predict thermophysical quantities, we compared the simulation results with experimental data. The results show that it is not possible to identify a tool that outperforms the others for all the analysed quantities, especially for the cavity air temperature, which is the least accurate parameter in all software due to underestimation of the daytime peak. IES-VE seems to be most accurate for Supply Air and Thermal Buffer modes when shading is deployed, while EnergyPlus appears most accurate for Outdoor Air Curtain mode. When it comes to surface temperatures and transmitted solar radiation, TRNSYS appears to be the best-performing software. In addition, this study investigated the challenges that designers may face when modelling a naturally ventilated DSF using whole-building simulation tools. Moreover, the investigation elucidates the challenges that have a more significant effect on the performance of the BES tools in order to reinforce their reliability.