Building Energy Performance Evaluation Research Articles

A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types

Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems.

Retrofit building energy performance evaluation using an energy signature-based symbolic hierarchical clustering method

Retrofitting existing buildings is crucial for significantly reducing energy consumption in the building sector. The continuous monitoring and evaluation of retrofit building energy efficiency is necessary to maintain optimal energy performance. This study proposes a novel method for evaluating building energy performance that combines energy signature analysis and hierarchical clustering. Symbolic data were defined by k-means using differences in gradients and y-intercepts before and after retrofitting extracted from energy signatures. Hierarchical clustering was then performed using the symbolic datasets. This symbolic hierarchical clustering method enhances the utility of open data and facilitates rapid decision-making. Additionally, it allows for a simple assessment of energy performance at the city scale. Through implementing this approach in 49 retrofitted buildings in Gangwon-do, South Korea, five types of symbolic data were identified (Types 0–4). Using hierarchical clustering, these buildings were clustered into six groups (Clusters 1–6). Type 3, representing ideal retrofitting outcomes, was observed in Clusters 2, 3, and 4 (73.47 % of all buildings). Conversely, Type 4 symbols, indicating a rebound effect, were observed in Cluster 1 and 2 (6.12 %). These findings provide meaningful information after retrofitting at the regional level, contributing to effective building management during retrofitting.

AutoBPS-Prototype: A web-based toolkit to automatically generate prototype building energy models with customizable efficiency values in China

Prototype building energy models are significant in building energy performance evaluation and energy efficiency potential analysis. However, their establishment process is time-consuming and error-prone. This study aims to develop a user-friendly toolkit, AutoBPS-Prototype, to automatically establish prototype building energy models in China. A web-based graphical user interface of AutoBPS-Prototype was designed and implemented. AutoBPS-Prototype only needs to define the climate zone, building type, and standard vintage to generate a default prototype building energy model based on a pre-built library. In addition, it can generate user-defined models through customized important efficiency values. The OpenStudio Standards used to create the U.S. Department of Energy prototype building models based on the ASHRAE 90.1 standards was extended to cover more prototype building energy models in China, including 20 sub-climate zones, 22 building types and four code and standard vintages. The Chinese building standards were used for the efficiency settings. Besides, case studies were conducted. Firstly, 20 prototype building models of four building types in five cities were generated. The credibility of the model results simulated by EnergyPlus was verified through comparison with measured data. Then, the impact of spatiotemporal differences on building energy consumption was analyzed through simulation of the models using different standard vintages in different climate zones. Finally, a baseline and four upgrade scenarios were designed to explore the impact of various upgrades on building energy consumption. This toolkit aims to assist users in conducting rapid research on building energy efficiency improvement. More toolkit features are under development for retrofit analysis, demand response analysis, and photovoltaics system generation estimation.

Time series prediction method based on sub-metering in building energy performance evaluation

Time series prediction method based on sub-metering in building energy performance evaluation

Simplified Model of Heat Load Prediction and Its Application in Estimation of Building Envelope Thermal Performance

This study provides a reference for estimating the building envelope thermal performance at the initial stage of design for nearly zero-energy buildings in different climate zones. A simplified model of heat load prediction, which combines the quasi-steady-state thermal balance calculation procedure in ISO 52016 and the variable-base degree-days method, was proposed. Therefore, a building energy performance evaluation tool BPT V1.0 was developed. Subsequently, the simplified model was validated through comparative analysis with the Building Energy Simulation Test (BESTEST) standard procedure. To conduct a feasibility analysis of the development tool, case studies were performed on the performance evaluation of building envelopes of residential and office buildings in different climate zones in China. Compared to the simulation results from EnergyPlus, the deviation of heat load calculated by BPT V1.0 was within 10%, which further verifies the applicability of the tool under different climatic conditions. Annual heat load under different thermal performance building envelopes was calculated through BPT V1.0. The building energy efficiency improvement rates were found to range from 30 to 60% in nearly zero-energy buildings in different climate zones in China. The study results can provide a reference for energy managers and a basis for estimating the building energy efficiency performance with different envelope thermal properties in the region.

New indicator for a comprehensive evaluation of building energy performance through spatial and temporal dimensions

Building functioning requires energy consumption. Traditional building energy performance (BEP) certification typically takes energy consumption as a single indicator, neglecting to evaluate the effect of energy application. Based on the definition of BEP and integrated consideration of energy consumption and service management, this study proposes a methodological path to construct a comprehensive performance indicator for buildings by evaluating energy consumption efficiency and amplitude of fluctuation using the XGBoost algorithm and objective assignment method. The indicator compares the deviation between actual and benchmark energy consumption in the spatial dimension and the amplitude of energy efficiency ratio fluctuations in the temporal dimension. Consequently, a comprehensive BEP evaluation model is thus coupled to form the comprehensive energy performance indicator (CEPI). The proposed approach is verified based on a city-scale dataset of over 2000 buildings, with CEPIi = 0.5288ECIi + 0.4712*MPIi for Chongqing. Compared with other evaluation systems, it is more intuitive, simple and robust, and can objectively reflect the rationality of energy use and provide a basis for scientific judgment.

Appropriate level of development of in-situ building information modeling for existing building energy modeling implementation

Existing buildings deteriorate over time, and the energy consumption continues to increase. In order to improve energy efficiency, evaluation of the energy performance of the existing buildings through simulation is required in advance. However, for most existing buildings, drawing information for evaluating energy performance is omitted. Without drawing information, the building energy cannot be modeled and energy cannot be predicted. To solve this problem, the building information modeling (BIM)-building energy modeling (BEM) process based on 3D laser scanning has recently been attracting considerable attention. When there is no drawing information in this process, geometric information of the existing building can be implemented through BIM. In addition, it is extremely important to determine the level of development (LOD) of the BIM because the accuracy of energy prediction depends on the LOD. In other words, the LOD of in-situ BIM should be determined for accurate building energy performance evaluation. Therefore, this study proposes an appropriate LOD of in-situ BIM for BEM implementation of existing buildings. Accordingly, the BIM-BEM process was applied and the building energy performance was evaluated. In addition, the uncertainty and accuracy were analyzed through comparison with the measured building energy consumptions. As a result of the analysis, LOD 350 was determined as a suitable LOD, capable of simulating the energy performance of an actual building.

머신러닝을 활용한 건물의 기밀성능 예측 모델 개발

In this study, as a building energy performance evaluation value correction concept, a building airtightness prediction model was developed using a machine learning algorithm capable of regression analysis based on building airtightness data collected from literature and field measurements. R², MAE, MSE, and RMSE were used to evaluate the accuracy of the prediction model, and the RReliefF algorithm was used to evaluate the contribution of the building elements used in predicting building airtightness. The random forest model had the highest prediction accuracy. Among the building elements, the wall-to-window ratio had the most effect on the airtightness performance.

Individualized empirical baselines for evaluating the energy performance of existing buildings

The evaluation of building energy performance requires a baseline for comparison. Common empirical baselines are usually used for existing buildings since they are fast and convenient. However, the same type of building at the same location will receive the same baseline despite their difference in usage. Individualized baselines by creating building energy models are possible solutions, but it is labor intensive and time-consuming. To fill the gap, this study is to develop individualized empirical baselines for existing buildings in a fast way. First, common empirical baselines are created based on survey data. Then, to get training samples, building energy models for large-scale existing buildings are created and simulated. Finally, based on simulation results, mathematical models to get individualized empirical baselines in a fast way are created. U.S. medium office buildings were used as an example to demonstrate the method. We developed 30 mathematical models for medium office buildings in two vintages (constructed before 1980 and after 1980) and 15 climate zones. The mean absolute percentage errors (MAPE) between the individualized empirical baselines and the modeled baselines for those 30 mathematical models are all lower than 5.5%. An engineer can obtain the individualized empirical baseline for an existing building in a few seconds by using the open-source tool we developed.

An experimental method for building energy need evaluation at real operative conditions. A case study validation

Building energy consumption reduction is fundamental to achieve sustainable green design and environmental impact lowering. Thermo-physics, different uses over time, heating/cooling plant and controlled mechanical ventilation system operation and users’ behaviour in the building affects energy saving conditions, indoor environmental quality and people wellbeing. In our research an experimental method for building energy performance evaluation under transient conditions was proposed. Monitoring experimental data of the external and internal microclimate, and of heat flux through each wall, under real dynamic conditions, were used for a real-time post-processing. The proposed method was based on the “modified” degree days calculated by the measured climatic data, taking into account the thermal phase shift and thermal capacity of the various construction materials. An experimental monitoring campaign was carried out for three years on a social housing development in Florence, as the case study. Results assessment provided a linear correlation function between the above mentioned parameters, with a coefficient of determination higher than 80%. The proposed method can be used for building energy consumption prediction that is fundamental for guaranteeing wellbeing, health and energy sustainability with the “green” transition from energy consuming community and buildings to energy producing community and buildings.

Building Energy Performance Evaluation of a Norwegian single-family house applying ISO-52016

In the following work, we have implemented a version of the hourly method in the ISO 52016-1:2017 standard, informed by the central input table in the SN/TS 3031:2021 specification, including a building energy supply system modelled according to the specification. A case study shows that the model compares well to measurements in unoccupied periods and that openly available gridded weather data can substitute data collected by the weather station on site. A more refined representation of boundary conditions and additional user inputs may be needed for other housing typologies than what can be recreated from the table, but we find that some of this information can be stipulated using open spatial datasets and tools. The results are presented in a web-service dashboard, maintaining continuity with operation phase data collection.

An integrated building energy performance evaluation method: From parametric modeling to GA-NN based energy consumption prediction modeling

Building energy performance evaluation, as an important process in a sustainable building design, has important consequences for global energy conservation and environmental protection. The traditional methods to perform this evaluation are usually time-consuming and computationally complex, and have high requirements for designers’ professional knowledge on architectural physics and software operation skills. To solve these problems and provide rapid, user-friendly, and more accurate prediction results, this study presents an efficient building energy performance evaluation method which integrates building information modeling, energy simulation, and energy consumption prediction together. This method follows a three-stage research framework: Stage 1 proposes a rapid 3D building energy modeling process according to the parameterized setting, Stage 2 generates numerous simulation results automatically by EnergyPlus, and Stage 3 develops the user-friendly building energy consumption prediction model with the help of the Genetic Algorithm-Neural Network (GA-NN) and provides the energy performance level of the building design after the prediction. A case study is carried out to present the overall process and verify the accuracy of the proposed three-stage building energy performance evaluation method. This study contributes to the improvement of both the extensive dataset establishment and the operational efficiency of building energy consumption prediction. It can provide designers with a real-time, user-friendly, and reliable building energy consumption prediction tool and an energy performance assessment basis in the design phase of construction projects.

Developing an optimization-based simulation approach for building energy performance evaluation (case study: Iran)

Developing an optimization-based simulation approach for building energy performance evaluation (case study: Iran)

Evaluation of Building Energy Performance with Optimal Control of Movable Shading Device Integrated with PV System

Among the envelope components (e.g., walls, roofs, floors, and windows, etc.) affecting the cooling and heating load of buildings, windows are the most thermally vulnerable. Shading devices can minimize the thermal load on windows due to solar radiation and decrease radiation effects. However, the load changes due to convection and conduction should be considered. Therefore, when a shading device is applied to a window, control logic for thermal blocking and heat retention is necessary to prevent the load changes. In addition, by combining the opposite features of photovoltaic (PV) that require solar radiation and the shading device to block solar radiation, energy-saving and production can be achieved simultaneously. Therefore, this study minimized the thermal effects of windows using a movable shading device integrated with PV and evaluated the PV power generation. This study evaluated the effects on window heat transfer by applying artificial intelligence techniques, which have recently attracted attention, to system operation. To achieve this, artificial neural network (ANN)-based control logic was developed, and the control performance of the system was assessed using simulations. In ANN control, the window heat transfer was 86.3% lower in a cooling period and 9.7% lower in a heating period compared with that of a shading device fixed at 45°. Furthermore, the PV system produced 3.0 to 3.1% more electric power under optimal control during the cooling period.

Improved energy performance evaluating and ranking approach for office buildings using Simple-normalization, Entropy-based TOPSIS and K-means method

Reasonable evaluation of building energy performance can provide great benefit for government (policy makers) to put forward effective energy conservation policies, and building regulators to make suitable energy-saving strategies. Traditional approaches employ the annual total energy consumption (or the annual total energy use intensity) to evaluate and rank the energy performance of buildings. However, these approaches ignore the energy consumption fluctuation of buildings in different seasons. As a consequence, this paper proposes an improved data-driven-based building energy performance evaluating and ranking approach for office building in city scale using the Simple-normalization, Entropy-based TOPSIS and K-means method. The proposed approach chooses the monthly energy use intensity in single year of office buildings as evaluation indicators, and consists of three steps: (1) The building energy use intensity are calculated by Simple-normalization method. (2) The Entropy-based TOPSIS method is applied to score and evaluate the building energy performance. (3) Finally, the K-means method is employed to rank the evaluated buildings. To validate the proposed approach, 24 office buildings in Tongling city of China are served as case study to present the evaluating and ranking procedure. Simultaneously, the comparison analysis between the proposed approach and two traditional approaches are conducted considering consistency and rationality synchronously. The comparison results demonstrate the proposed approach can more effectively score the energy performance of office buildings, and achieve the reasonable order and grade. In addition, to demonstrate the application, the proposed approach is also applied on Urban-area Building Energy Consumption Monitoring System of Tongling city in China. As a result, this study provides convenient and effective tool to present the energy efficiency gap and energy-saving potential between different buildings in city scale.

Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.

Need for integrated approach for energy efficiency assessments and material evaluation of historic buildings

Abstract Concerns about the energy sustainability of the existing building stock and about changes in both climatic conditions and comfort requirements, in recent years have put a lot of research focus on energy performance evaluation of buildings, which also need detailed assessment of material properties. Most often, historic masonry buildings present both structural and material typicality owing to local material availability and technical know-how at time of construction. Further, the available energy efficiency assessment methodologies demand a detailed diagnostic of the material behaviour. Along with this, the heritage status and the conservation policies attached to these buildings pose special requirements of on-site measurement and non-invasive analytical tools. Presently, as carried out, commonly the evaluation of all these aspects is undertaken separately. The paper highlights the need for an integrated investigative approach considering the need of material investigations and input requirements of advanced analytical tools like the simulation software systems. The paper also highlights the available literature references and practices presently followed in the energy efficiency studies for documenting material properties and identifies a vast scope for further study in the domain.

A learning-based time-efficient framework for building energy performance evaluation

Recently, detailed building energy models are being widely used for building control assessments. When conducting such assessments, year-long simulations are usually deployed to cover as many building operation conditions as possible. However, year-long simulations can become computationally cumbersome, given the inherent nonlinearities of high-fidelity, detailed building models. In this paper, we propose a novel, time-efficient, and data-driven framework for selecting specific weather conditions when assessing building energy performance within a simulation environment. In the proposed framework, learning-based methodologies are used to group days of a performance assessment time window (usually a whole year) which are “similar” in terms of weather conditions (such as temperature, humidity, precipitation etc.) or contexts (such as “day of the week”, “weekday/weekend”, etc.) into different clusters. Following this, we select a finite number of “representative days” from each of these clusters, using which we try to understand the correlations between building performance and weather (or contexts) for each specific cluster. Subsequently, this knowledge is used to predict the building performance for the remaining “non-representative days”, and in turn estimate the overall annual energy performance of the whole building. We demonstrate the performance of the proposed framework on a varied suite of buildings across different geographical locations of the U.S. Results suggest that for practical purposes, our proposed approach can reduce the computational cost while maintaining considerable accuracy, compared to year-long simulations.

Correlation between building characteristics and associated energy consumption: Prototyping low-rise office buildings in Shanghai

The enormous building energy consumption in Shanghai necessitates the identification of standard buildings to offer guidance for the adequate design of retrofitting strategies in order to promote a sustainable built and architectural environment. In this regard, this study develops a methodological approach to establish prototypical buildings using performance index system (PIS) founded on an on-site survey. Emphasis is focused on low-rise office buildings in Shanghai. A total of 10 office parks containing 136 single low-rise office buildings in Min Hang District were systemically selected for survey and data collection. The proposed PIS includes building orientation, number of floors, window/wall ratio, heat and cold source type, plan form, and construction year. Using cluster and correlation analysis, the surveyed buildings are classified based on the impact of each PIS on the annual building energy use intensity. Based on this approach, the most influencing indexes are construction year, the number of floors, window-wall ratio and building orientation. This result refines the surveyed building samples to four prototypical buildings as representative standards for low-rise office buildings. Subsequently, typical buildings representing each of the prototypical buildings were defined. The stipulated approach provides a systematic framework for building classification, characteristic-based evaluation of building energy performance and identification of key performance index for building retrofit purposes.

Residential building energy performance evaluation for different climate zones

The building sector in the Kingdom of Saudi Arabia accounts for a large proportion of energy consumption. This paper investigates energy consumption in residential type A villa buildings constructed by the Ministry of Housing. The study specifically investigates energy consumption of a case building in three climate zones of the country identified in the KSA Building Code (SBC-602E). The software package IES-ve was used to simulate power consumption of the case building taking into account the climate characteristics of each zone. In total 6 cities were considered from the three zones to evaluate and compare annual and monthly power consumption loads of the building. It was found that there is some distinction of energy consumption for heating and cooling in the three zones (e.g., peak monthly power energy consumption for cooling varied from 6.371 MWh in Riyadh (zone1) to 1.663 MWh in Abha (zone3) and no heating requirement for Jaddah (zone 1) while Turaif (zone 3) heating consumption is 23.835 MWh) however, further work is required to identify clearer the climate specifications within each zone to address the excess energy consumption of this type of buildings.