Building Energy Performance Analysis Research Articles

Early energy performance analysis of smart buildings by consolidated artificial neural network paradigms

The assessment of energy performance in smart buildings has emerged as a prominent area of research driven by the increasing energy consumption trends worldwide. Analyzing the attributes of buildings using optimized machine learning models has been a highly effective approach for estimating the cooling load (CL) and heating load (HL) of the buildings. In this study, an artificial neural network (ANN) is used as the basic predictor that undergoes optimization using five metaheuristic algorithms, namely coati optimization algorithm (COA), gazelle optimization algorithm (GOA), incomprehensible but intelligible-in-time logics (IbIL), osprey optimization algorithm (OOA), and sooty tern optimization algorithm (STOA) to predict the CL and HL of a residential building. The models are trained and tested via an Energy Efficiency dataset (downloaded from UCI Repository). A score-based ranking system is built upon three accuracy evaluators including mean absolute percentage error (MAPE), root mean square error (RMSE), and percentage-Pearson correlation coefficient (PPCC) to compare the prediction accuracy of the models. Referring to the results, all models demonstrated high accuracy (e.g., PPCCs >89%) for predicting both CL and HL. However, the calculated final scores of the models (43, 20, 39, 38, and 10 in HL prediction and 36, 20, 42, 42, and 10 in CL prediction for the STOA, OOA, IbIL, GOA, and COA, respectively) indicated that the GOA, IbIL, and STOA perform better than COA and OOA. Moreover, a comparison with various algorithms used in earlier literature showed that the GOA, IbIL, and STOA provide a more accurate solution. Therefore, the use of ANN optimized by these three algorithms is recommended for practical early forecast of energy performance in buildings and optimizing the design of energy systems.

Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach

Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures.

옥상온실형 건축물 리모델링을 통한 에너지요구량 분석 : 성능향상 시뮬레이션 분석 및 다항식 예측모델을 통한 기여도 분석

옥상온실형 건축물 리모델링을 통한 에너지요구량 분석 : 성능향상 시뮬레이션 분석 및 다항식 예측모델을 통한 기여도 분석

Investigating the disparities between experimental and computational analyses of thermal performance in prefabricated wall panels

Prefabricated buildings are increasingly popular worldwide, driven by technological advancements and the demand for sustainable and energy-efficient construction. However, the thermal performance of typical multi-layered and compacted prefabricated wall panels remains uncertain, leading to overdesigned HVAC and building envelope systems. This results in higher utility bills, unnecessary expenses, and increased material, labor, and transportation costs. While experimental analysis and computational simulations have proven effective for measuring the thermal performance of on-site wall panels (e.g., precast concrete, masonry), their effectiveness for exterior insulation finishing system (EIFS) prefab wall panels is unclear. This study aims to test the hypothesis that both experimental and computational analyses accurately measure the thermal performance of EIFS prefab wall panels under various climate conditions and yield to similar results. To achieve this, (1) experimental test analysis was conducted in an environmental chamber based on ASTM C1363 and ISO 8990 standards, and (2) computational simulation was conducted by employing THERM® at hot (65 °C) and cold (−16 °C) climates. The results showed that the experimental thermal resistance exceeded the computational thermal resistance by about 17% across hot and cold climates. These thermal resistances were then incorporated into a typical hospital building prototype in the U.S. modeled by U.S. Department of Energy in EnergyPlus™, resulting in substantial cost savings of approximately $396,000 over a 20-year lifespan due to the thermal resistance variations between experimental and computational analyses. The savings were achieved through a reduction in construction costs, accurate HVAC system selection, and lower utility bills. This study contributes to the improvement of prefabricated building design and energy performance analysis, leading to potential cost reductions in HVAC and building envelope system overdesign.

Simulation-based comparative analysis of U-value of field measurement methods

Accurate analysis of building energy performance necessitates methodologies that can diagnose the thermal performance of a building's envelope. The building envelope contains various uncertainties and poses difficulties in verifying the measurement results, making it crucial to ensure the reliability of the measurement method. This study used dynamic simulations to verify the accuracy and reproducibility of commonly used field measurement methods. The simulation data were applied to the heat flux meter (HFM) and infrared thermography (IRT) methods to calculate the thermal transmittance of the building envelope and confirm their suitability as a field measurement method. According to the accuracy verification results, the HFM and indoor IRT (IRTi) methods, which are less affected by the external environment, evaluated the actual thermal performance of the wall close to the theoretical value with average relative errors of 3.3% and 4.2%, respectively. In the reproducibility evaluation, the HFM method exhibited similar levels of deviation over time. Additionally, to reduce the deviation in the reproducibility of the thermal transmittance derived from the IRT method, the average method was applied for data analysis, leading to a decrease in the reproducibility deviation from 36.5% to 13.3% for IRTi and from 107.3% to 71.8% for IRT outdoors (IRTo).

Predicting Building Energy Demand and Retrofit Potentials Using New Climatic Stress Indices and Curves

Building energy requalification in Italy and Europe has been much discussed in recent years due to the high percentage of existing buildings with poor energy performance. In this context, it is useful to obtain a user-friendly and fast tool to predict the thermal energy demand (TED) for space conditioning and the related primary energy consumption (PEC) as a function of climatic stress. In this study, the SLABE methodology (simulation-based large-scale uncertainty/sensitivity analysis of building energy performance) is used to simulate representative Italian buildings, varying parameters such as geometry, envelope and HVAC (heating, ventilating and space conditioning) systems. MATLAB® in combination with EnergyPlus is used to analyze 200 buildings belonging to two structural types (multi-family buildings and apartment blocks) built in 1961–1975. Nine scenarios (as-built scenarios and eight retrofit ones) are investigated in 63 climatic locations. A regression analysis shows that the classical HDDs (heating degree days) approach cannot give an accurate prediction of TED because solar radiation is not accounted for. Thus, new climatic indices are developed alongside solar radiation, including the heating stress index (HSI), the cooling stress index (CSI) and the yearly climatic stress index (YCSI). The purpose of our work is to obtain climatic stress curves for the prediction of TED and PEC. Testing of this novel approach is performed by comparison with another building energy simulation tool, showing a low discrepancy, i.e., the coefficient of variation of the root mean square error is between 12% and 28%, which confirms certain reliability of the approach here proposed.

Proposal of retrofit of historic buildings as cafes in Korea: Recycling biomaterials to improve building energy and acoustic performance

While the preservation of historic buildings is important, they must remain usable. The comfort of occupants using the historic building must be considered due to the change of use. In this study, a 100-year-old building in Korea, protected as a cultural heritage site, is retrofitted. Buildings currently in continuous use as cafes require improvement of low building performance due to aging materials. The energy performance and sound environment of the target building were improved by recycling café generated coffee waste. Recycled coffee waste uses a phase change material, increasing its latent heat. DesignBuilder simulation, a building energy performance analysis software, analyzed energy consumption and room temperature before and after retrofit. C10 containing 10% of the phase change material of coffee waste board showed high efficiency, saving heating energy after retrofit. After retrofit according to the material sound absorption coefficient, ODEON simulation confirmed improvement of the indoor sound environment. The measured sound pressure level was 73.95 and 75.15 dB in the 500 and 1,000 Hz frequency bands, respectively; however, after implementing improvements, the sound pressure level decreased by up to 7.39%. Reverberation time was observed as high as 1.47 s and 1.83 s for the 500 and 1000 Hz frequency bands, respectively, and decreased by up to 66.67%. Therefore, application of coffee waste board to the retrofit of historic buildings is effective in improving indoor environment.

Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance

Proper analysis of building energy performance requires selecting appropriate models for handling complicated calculations. Machine learning has recently emerged as a promising effective solution for solving this problem. The present study proposes a novel integrative machine learning model for predicting two energy parameters of residential buildings, namely annual thermal energy demand (DThE) and annual weighted average discomfort degree-hours (HDD). The model is a feed-forward neural network (FFNN) that is optimized via the electrostatic discharge algorithm (ESDA) for analyzing the building characteristics and finding their optimal contribution to the DThE and HDD. According to the results, the proposed algorithm is an effective double-target model that can predict the required parameters with superior accuracy. Moreover, to further verify the efficiency of the ESDA, this algorithm was compared with three similar optimization techniques, namely atom search optimization (ASO), future search algorithm (FSA), and satin bowerbird optimization (SBO). Considering the Pearson correlation indices 0.995 and 0.997 (for the DThE and HDD, respectively) obtained for the ESDA-FFNN versus 0.992 and 0.938 for ASO-FFNN, 0.926 and 0.895 for FSA-FFNN, and 0.994 and 0.995 for SBO-FFNN, the ESDA provided higher accuracy of training. Subsequently, by collecting the weights and biases of the optimized FFNN, two formulas were developed for easier computation of the DThE and HDD in new cases. It is posited that building engineers and energy experts could consider the use of ESDA-FFNN along with the proposed new formulas for investigating the energy performance in residential buildings.

Occupant Behavior Impact on Building Sustainability Performance: A Literature Review

Occupant behavior controls a building’s energy system to adapt the indoor environment, significantly increasing building energy consumption. Occupant behavior, which refers to the occupancy inside a building and their interaction with building systems (windows, blinds, thermostats, lighting and appliances, etc.), has been largely overlooked in building energy performance analysis. These factors make it essential to design sustainable buildings. It is widely acknowledged in the literature that there is an alarming performance gap between the estimated and actual energy consumption in buildings. This paper proposes a systematic literature review on energy-related occupant behaviors and their implications for energy performance. It aims to better understand occupant behavior, existing behavior modeling approaches and their limitations, and key influential parameters on building energy performance. It is based on a survey of ScienceDirect, Web of science and Scopus scientific databases, using their bibliometric analysis tools together with the VOSviewer software. Finally, this study identifies the following significant research gaps for future development: limitations of the generic and robust occupant behavior model; lack of actual data for validation; lack of research on different types of buildings (institutions, university buildings); limitations of considering all factors which influence occupant behavior; missing the detailed realistic situations of occupant behavior; integrating building information modeling (BIM) into building energy modeling.

A Suggestion of the Alternatives Evaluation Method through IFC-Based Building Energy Performance Analysis

In a rapidly changing modern society, the construction industry is facing various issues, including the Fourth Industrial Revolution and climate change. Research on convergence between technologies such as artificial intelligence, AR/VR, IoT, and metaverse, and sustainable technologies such as green buildings and eco-friendly energy is being attempted in each field. The most important thing in the development of these technologies will be the interoperability of data. BIM is a technology that can effectively store data regardless of the size of a building or the amount of information and can be shared and stored without loss of data through an open format called IFC (industry foundation classes). This study aims to present a plan to generate alternatives and evaluate energy performance by analyzing the shape of the envelope for amorphous buildings through IFC. Design elements were derived through analysis of previous studies, and alternatives were automated by developing interfaces that can generate shapes according to the derived design elements. The generated alternatives can be compared and analyzed through the analysis of building energy by developing an evaluation system based on IFC. Based on the quantitative results in the initial design stage, the reliability of the design proposal considering the performance of the building is improved, and the process and cost can be predicted in advance; thus, it is expected to be an efficient decision support tool.

The Analysis of Tall Building Energy Performance by Using EDGE Application towards Net Zero Energy Building (NZEB)

Net Zero Energy Building (NZEB) is one of the innovative concepts for sustainable buildings because of its ability to save energy and reduce carbon emissions simultaneously. This study used a descriptive approach to review tall building data through the parameter of saving electricity by using the EDGE application. Primary data is data obtained from working drawings or detailed engineering design (DED), work plans and specifications, and budget plans. EDGE application also produced several outputs to analyze energy performance: final energy use, operational CO2 emissions, embodied energy, utility cost, cooling loads, energy savings, operational CO2 savings, cost savings, annual EPI base case, improved EPI, number of affected people, base case and improved global warming potential of refrigerants. The EDGE also analyzed the following data: building type, site location, project details, building utility data, occupational and operational information, building dimension and specification, HVAC system, fuel used, and climate data. The energy performance of the building is analyzed by using EDGE’s parameters coded by EEM 01-37 that span from the windows-to-walls ratio to the use of low environmental impact refrigerant.

Integrated computational approaches for energy retrofit of historical buildings in extreme climate environments

PurposeThe purpose of this research is to test the effectiveness of integrating Grasshopper 3D and measuring attractiveness by a categorical based evaluation technique (M-MACBETH) for building energy simulation analysis within a virtual environment. Set of energy retrofitting solutions is evaluated against performance-based criteria (energy consumption, weight and carbon footprint), and considering the preservation of the cultural value of the building, its architectural and spatial configuration.Design/methodology/approachThis research addresses the building energy performance analysis before and after the design of retrofitting solutions in extreme climate environments (2030–2100). The proposed model integrates data obtained from an advanced parametric tool (Grasshopper) and a multi-criteria decision analysis (M-MACBETH) to score different energy retrofitting solutions against energy consumption, weight, carbon footprint and impact on architectural configuration. The proposed model is tested for predicting the performance of a traditional timber-framed dwelling in a historic parish in Lisbon. The performance of distinct solutions is compared in digitally simulated climate conditions (design scenarios) considering different criteria weights.FindingsThis study shows the importance of conducting building energy simulation linking physical and digital environments and then, identifying a set of evaluation criteria in the analysed context. Architects, environmental engineers and urban planners should use computational environment in the development design phase to identify design solutions and compare their expected impact on the building configuration and performance-based behaviour.Research limitations/implicationsThe unavailability of local weather data (EnergyPlus Weather File (EPW) file), the high time-resource effort, and the number/type of the energy retrofit measures tested in this research limit the scope of this study. In energy simulation procedures, the baseline generally covers a period of thirty, ten or five years. In this research, due to the fact that weather data is unavailable in the format required in the simulation process (.EPW file), the input data in the baseline is the average climatic data from EnergyPlus (2022). Additionally, this workflow is time-consuming due to the low interoperability of the software. Grasshopper requires a high-skilled analyst to obtain accurate results. To calculate the values for the energy consumption, i.e. the values of energy per day of simulation, all the values given per hour are manually summed. The values of weight are obtained by calculating the amount of material required (whose dimensions are provided by Grasshopper), while the amount of carbon footprint is calculated per kg of material. Then this set of data is introduced into M-MACBETH. Another relevant limitation is related to the techniques proposed for retrofitting this case study, all based on wood-fibre boards.Practical implicationsThe proposed method for energy simulation and climate change adaptation can be applied to other historic buildings considering different evaluation criteria and context-based priorities.Social implicationsContext-based adaptation measures of the built environment are necessary for the coming years due to the projected extreme temperature changes following the 2015 Paris Agreement and the 2030 Agenda. Built environments include historical sites that represent irreplaceable cultural legacies and factors of the community's identity to be preserved over time.Originality/valueThis study shows the importance of conducting building energy simulation using physical and digital environments. Computational environment should be used during the development design phase by architects, engineers and urban planners to rank design solutions against a set of performance criteria and compare the expected impact on the building configuration and performance-based behaviour. This study integrates Grasshopper 3D and M-MACBETH.

Optimization of a diabatic compressed air energy storage coupled with photovoltaics for buildings: CO2-eq emissions vs payback time

The necessary path towards sustainable development makes increasingly crucial the role of energy storage systems because the most affordable renewable energy sources (RES) are typically intermittent In this perspective compressed air energy storage (CAES) is one of the main alternatives to batteries and pumped hydro energy storage. This study shows how an integrated system coupling RES and CAES may significantly reduce greenhouse gas emissions with acceptable payback times. The electrical load is provided by a representative building sample (RBS) located in Naples (Southern Italy), generated using the simulation-based large-scale uncertainty/sensitivity analysis of building energy performance (SLABE) methodology. A Pareto multi-objective optimization is performed via a brute-force search to minimize CO2-eq emissions and payback time. The design variables refer to a photovoltaic system design and to size and operation of an associated diabatic CAES. Two different application scenarios are envisaged: 1000 buildings and 2000 buildings. The avoided greenhouse gas emissions are about 55% and 51% with simple payback times of 14.3 years and 12.4 years, respectively. The results show that the proposed integrated framework, with the right mix of renewables, may lead to the complete satisfaction of the electrical load at reasonable costs.

Data-driven prediction of building energy consumption using an adaptive multi-model fusion approach

This paper develops an adaptive multi-model fusion approach to predict building energy consumption, aiming to give useful suggestions for better energy control. The building energy benchmarking dataset of Chicago in 2017 is selected as the case study, where 9 features are selected as the input variables aiming to estimate the weather normalized site energy use intensity of buildings. The training dataset is clustered using the K-means algorithm and sub-models are trained based on the clustered data using the XGBoost algorithm. The sub-models are then fused by assigning a weight considering both the model reliability and the matching degree and adopting a screening algorithm to weed out the unmatching sub-models, where the influence of the threshold in the screening algorithm is studied. The root mean square error of the estimation results from a fused model is found to be 13.42 which achieves a 7.6% amelioration compared with a single model. Moreover, the adaptive multi-model fusion approach is also proved to outperform both the two-stage clustering-based regression method and the linear fusion method. Benefiting from proper treatment of samples in the fuzzy zones between clusters and the screening algorithm in the fusion process, the method proposed in our paper eventually serves as more advanced guidance in the analysis and control of building energy performance.

Energy Performance Analysis of a Multi-Story Building Using Building Information Modeling (BIM)

There is an enormous rise in building construction to meet serious demands of population increase. Besides its benefits, certain negative impacts on climate change and environments are associated with the built environment due to substantial energy requirements during operational phase. The current work aims to assess the energy consumption pattern of a residential facility based upon solar path analysis using simulation technique. A multi-story conventional building has been developed in a virtual 3D parametric environment using building information modeling. The BIM model was converted into the energy model using cloud computing. The energy model, at the proposed current orientation, was analyzed using insight 360 and solar energy analysis performed accordingly. Based upon the solar path analysis, the study observed that, at the present trajectory of solar path, provision of solar panels arrangements on 106,221 ft2 Photovoltaic panel area can produce the energy of 2,163,417 kwh/year with a payback period of 0.8 years.Keyword: Building Information Modelling, Energy analysis, Energy Optimization, Architecture 2030 Challenge

Energy Performance Analysis of Buildings in Terms of Alternative Reinforced Concrete Structural Systems

In this study, the thermal energy performances and environmental effects of two different reinforced concrete structures, Pure Shear Wall (PSW) Building and Shear Wall-Frame (SWF) Building, were investigated in the climatic conditions of Elazig province. Thermal performance analysis for both buildings; was carried out with the TS 825 program. First of all, through this program, monthly and annual heating loads were determined for 11 different alternative building component scenarios of two different buildings. Subsequently, efficiency alternatives were created in accordance with the climatic conditions of the region and TS 825. Efficiency alternatives designed for both buildings have been analyzed together with their completely uninsulated and current states. Finally, buildings with reinforced concrete carrier systems, energy analysis methods and efficiency alternatives were compared and evaluated based on the results of the analysis. As a result, it has been observed that the building with the PSW system consumes more energy than the building with the SWF system when it is uninsulated and in its current condition.

Building energy performance analysis at urban scale: A supporting tool for energy strategies and urban building energy rating identification

The energy consumption of the building sector has complex and interrelated characteristics. The development of modeling techniques capable of evaluating the impacts of energy-efficient technologies requires great effort owing to the variety of building sizes and geometries, wide range of thermal properties of building envelopes, variable occupant behavior, and lack of measured consumption data. However, energy policies require thorough knowledge of the energy performance of a building stock to be truly effective. In this paper, an urban-scale building energy modeling tool based on an engineering bottom-up approach is presented. The employed methodology makes it possible to assess the energy performance of a building stock, define energy efficiency measures, and evaluate their effects automatically using a geographic information system. Moreover, the modeling tool introduces an innovative urban building energy rating similar to the energy rating of individual buildings. The proposed energy modeling tool is then applied to a real case consisting of three villages in central Italy with 769 residential buildings.

Energy performance analysis of a historical building using cost-optimal assessment

The energy efficiency concept has attracted significant attention over the last few years and has taken a prominent role in the development goals of countries. Considering that buildings have a high share in total energy consumption, it is crucial to improve their energy performance both in environmental and economic terms. Although efforts in increasing buildings’ energy efficiency have primarily focused on new buildings, existing buildings still retain their importance. Historical buildings are included in the existing building stock. It is essential to increase their energy efficiency to achieve carbon–neutral building targets set from EU Directives. In this paper, we proposed measures to improve the energy efficiency and determine cost‐optimal levels of a public historical building located in a hot-humid climate zone of Turkey. An integrated approach based on in situ measurements and building energy simulations was used to create a building energy model that was calibrated by using real measurements. Then, 365 energy-efficient measures are applied to the building and conducted an economic feasibility study by utilizing a cost-optimal envelope, lighting, heat pump and photovoltaic panel. A sensitivity analysis is performed to determine the impact of investing on optimal cost-of-energy level calculations with focus on the inflation rate, different interest rates, and calculation periods. Sensitivity analysis of discount rate has shown when the discount rate increases, global costs decrease. It is observed that the changes in economic parameters that could cause a decrease in annual costs reduced the cost effectiveness of the scenarios with high initial investment costs.

The Impact of Climate Change on the Energy Consumption of Passive Residential Buildings in the Yangtze River Delta

With the global warming in the future, the meteorological data based on the Typical Meteorological Year (TMY) can no longer accurately reflect the changes in building energy consumption under future meteorological conditions. This paper selects the typical meteorological year (TMY) in the Yangtze River Delta region as the base year data and the monthly scale forecast data provided by the IPCC, and uses the “Morphing” method to predict hourly weather data in 2050 and 2080. The use of energy simulation software Energy Plus of the Yangtze River Delta region in a passive multi-storey residential building energy simulation, analysis of building energy performance under future climate change, projected changes passive residential building energy demand over the next 60 years of trend.

Building Energy Performance Analysis after Changing Its Form of Use from an Office to a Residential Building

Lowering energy consumption is one of the most important challenges of the modern world. Since the construction sector accounts for 40% of total energy consumption worldwide, the Parliament of the European Union has developed a Directive, according to which all newly designed and thermally upgraded buildings should meet the requirements of almost zero energy demand (nZEBs) from 1 January 2021. At the same time, in Poland, but also in many other countries in the world, there is a growing oversupply of office space in large cities with a simultaneous increase in demand for apartments, which is caused, among other things, by the increase in the share of remote work and the COVID-19 pandemic. Consequently, this is forcing owners to change the use of buildings from office to residential. This article analyses the possibilities of changing the function of an office building to a multi-family residential building. For both functional solutions, a comparative energy analysis was carried out, taking into account different work schedules and the requirements for new buildings with zero energy demand. The analyses have shown that changing the form of use of an office building to a multi-family building without significant financial and technical costs is possible. On the other hand, the reverse change of the form of use is much more difficult and, in many cases, practically impossible. Due to the fact that many offices are now multinational corporations, this issue is global.