Improve Building Energy Performance Research Articles

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

Annual evaluation of the visual-thermal comfort and energy performance of thermotropic glazing in a reference office room of China

Hydrogel-based thermotropic glazing, which achieves the dynamic characteristics by maneuvering the light-scattering behaviors, has a great potential in improving building energy performance, visual and thermal comfort. The visual-thermal comfort and energy performance of the thermotropic glazing with different transition temperature was evaluated by using an experimental validated building performance simulation model. The performance of the thermotropic glazing was compared with conventional double-clear glazing and low-emissivity double glazing under five cities within the five major climate zones across China. The objective is to comprehensively understand the applicability of the thermotropic glazing in different climatic conditions of China as well as to determine the optimal transition temperature. The results showed that: (1) The thermotropic glazing with a transition temperature of 30 °C could achieve better energy performance, visual and thermal comfort in Xiamen, Nanjing, and Kunming. (2) Low transition temperature is not an essential requirement for the application of the thermotropic glazing, which could result in increasing energy use intensity and this effect is more significant in colder regions. (3) The thermotropic glazing could contribute to building energy savings (up to 16.3% and 2.3% i.e., in Xiamen) compared with conventional double-clear glazing and low-emissivity double glazing with low SHGC. (4) The thermotropic glazing applied in Xiamen, Nanjing and Kunming could obtain better visual and thermal comfort (i.e., increase of the desired range of illuminance sUDI500–2000lux, is up to 85.19% and a promotion of 29.18% in time proportion of desirable thermal comfort in Kunming) when compared with low-emissivity double glazing and conventional double-clear glazing.

Predictive Model for Optimal Growth and Benefits of Exterior Building-Integrated Vegetation

Vegetated surfaces in urban environments have the potential to improve building energy performance, reduce urban heat islands (UHI), and establish habitat connectivity between natural areas. However, most design and simulation tools lack integrated metrics to assess habitat fragmentation, suitable growing environments for plant species, or overall potential for biodiversity within urban environments. This research established a modeling framework that synthesizes data input on plant species, urban context, and climatic factors to forecast the habitat connectivity, select microclimatic conditions, and potential biodiversity benefits of integrating vegetated surfaces within urban environments. Utilizing ‘reconciliation ecology,’ the modeling framework combines design feedback on green roofs and walls to reduce habitat fragmentation, promote biodiversity, and create the potential for cities to support a range of threatened or endangered species.

Improving building energy performance based on building information modelling approach

Improving building energy performance based on building information modelling approach

Evaluation and analysis of transmitted daylight color quality for different colored semi-transparent PV glazing

Windows are one of the minor energy-efficient building elements, and PV windows can replace conventional windows to improve building energy performance. This study explored the color quality of transmitted light from 24 types of semi-transparent PV glazing, considering the color rendering index (CRI-Ra), gamut index (Rg), color fidelity index (Rf), and correlated color temperature (CCT). PV glazing in this study had four different transparencies (i.e., 20 %, 40 %, 60 % and 80 %) and six colors. The results indicate that different transparencies have little impact on the color quality of the transmitted light, but there are variations in PV glazing with different colors. PV glazing with different colors Ra ranging from 48.5 to 98.1, Rf ranging from 0.9 to 98.4, Rg ranging from 76.4 to 170.8, and CCT ranging from 1036.8 to 7948.8 K. This study summarized the feasible application scenarios of different colored semi-transparent PV glazing from indoor visual activities and human-centric point of view. The energy savings of PV glazing ranged from −43.88 % to 35.55 % in five climate zones in China. Lower PV transmittance leads to higher building energy consumption. The most energy-efficient PV glazing is PV-Regular 40 %, and the least is PV-Green 20 %.

Application of sensitivity analysis on building dynamic lifecycle assessment of GHG emissions: a French case study

This study presents a novel approach to simplify the dynamic life cycle assessment (DLCA) of buildings by identifying and prioritizing influential dynamic parameters (DPs) to improve building energy performance and reduce greenhouse gas emissions. Current life cycle assessment (LCA) methodologies lack temporal considerations, which can significantly impact a building’s environmental footprint over its multi-decade life span. We conducted an extensive literature review on DPs in DLCA, informing the creation of a multi-scenario parametric framework. A case study was then selected to apply the developed DLCA methodology and perform a global Sensitivity Analysis to pinpoint the most influential DPs on global warming potential. The DLCA framework integrates data from the French database for environmental product declarations and utilizes EnergyPlus simulations for operational emissions assessment. The study identifies ten DPs, revealing the most impactful ones to be industry and waste sector emissions reductions, building occupancy, and global warming. Additionally, we found minimal interactions between DPs, which facilitates the simplification of the DLCA process. This research enables building stakeholders to focus on a more selective set of parameters, enhancing the efficiency and accuracy of building life cycle assessments. Overall, our findings contribute to the continuous improvement of LCA methodologies and promote sustainable building practices.

Experimental and numerical investigations on the influence of phase change materials on heat transfer reduction across the roof of a building

ABSTRACT Faced with the problems of increasing energy scarcity and ongoing environmental deterioration, phase change materials (PCM) have attracted a lot of attention recently for improving building energy performance. Utilizing the PCMs in buildings is one of the efficient approaches to reduce the heat penetration into the buildings. In this work, the thermal performance of a building equipped with PCM roof is analyzed and compared with a plain roof building. The uniqueness of this study is to analyze the effect of PCM experimentally and numerically through parametric investigation with different PCMs (HS29 and OM29), encapsulation materials (aluminum and steel) and PCM thickness. The experimental analysis shows that the PCM incorporated roof decreased the inner room temperature of the building by 2°C vis-à-vis plain roof and hence helps reduce the cooling load and enables passive cooling of the building with energy savings. A numerical analysis was carried out using COMSOL and validated with maximum and average errors of 7.43% and 4.87% respectively. The parametric investigation from the validated model indicates that the PCM OM29 is effective compared to HS29 in terms of temperature reduction. The steel encapsulating panels are observed to be better than aluminum at the peak sunshine hours while the aluminum panel is effective for the other periods. It is observed that the increase in PCM thickness augments the reduction in heat transfer while the 6 cm PCM thickness showed a higher temperature reduction by 3.6°C vis-a-vis 2 cm PCM thickness.

Implementing a Web-based Optimized Artificial Intelligence System with Metaheuristic Optimization for Improving Building Energy Performance

ABSTRACT Improving energy efficiency in buildings is a challenge during operation and maintenance. The work proposes a cloud artificial intelligence-based building energy management (cloud AI-BEM) system for predicting building energy consumption. The proposed system includes the data layer, the AI-analytics layer, and the decision support information layer. The data layer collects and stores data in the cloud database management system. The analytics layer performs applied a hybrid AI model which was developed and deployed in this layer that enables predict future energy consumption in buildings. The hybrid AI model, namely the SAMFOR model was developed based on the integration of the seasonal autoregressive integrated moving average (SARIMA) model and the firefly algorithm (FA) and least-squares support vector regression (LSSVR). The web-based layer visualizes insights for users. As insights, the cloud AI-BEM system enables to monitor and to compare the energy consumption among buildings; to predict one-day-ahead energy use in buildings, to produce key performance indicators of energy use; to visualize energy data and outdoor temperature data; to easily interact with data from the system interface. Average accuracy in terms of RMSE values ranged from 1.36 kW per 30 mins. The R values were higher than 0.957 and very close to 1.

Optimization Bundle Paths of the Building Envelope for Zero-Carbon Strategies

Improving building energy performance with minimum emission and cost is important for zero-carbon strategies. In this regard, this study mainly focuses on the envelope of an apartment block. The aim is to investigate the tripartite relationship between energy performance, CO2 emission, and cost by using different wall and roof insulation materials, and various glazing types in a typical reinforced concrete five-floor apartment block in Istanbul, Turkey. In a building performance simulation tool - Cove. Tool, material alternatives’ impacts on energy use, cost, and CO2 emission are calculated. Consequently, 32 different design bundles are generated. Finally, the most advantageous material combination is explored from the design combinations to make cost-conscious, performance-driven, and environmentally-friendly decisions.

Preliminary study on the use of Big Data for environmental benchmarks of residential buildings in Flanders

Building construction and operation both have a high environmental impact. In Flanders (Belgium), public authorities have defined clear targets for improved building energy performance, but a strategy to reduce construction (embodied) impact is still lacking. Environmental benchmarks based on Life Cycle Assessment (LCA) have been identified as a means to limit embodied impacts. Such benchmarks are often derived with a bottom-up approach consisting of a statistical analysis of the building stock, which is usually modelled based on a limited set of representative buildings or archetypes. In this paper, a data-driven approach is applied based on building data from the Flemish Energy Performance of Buildings (EPB) database. In a recent study, the buildings from the EPB database were clustered based on geometric and energy-related parameters, and for each cluster representative buildings were selected. This resulted in 54 buildings representative of newly built residential buildings in Flanders. The building set distinguishes itself from other existing sets because it was automatically generated from a large building database. Up until now, the EPB building set has only been used to evaluate the financial feasibility of energy performance levels in Flanders. In this preliminary study, an LCA is performed to assess the life cycle environmental impacts of five sample cases in view of benchmarking. The sample includes two detached, two semi-detached, and one terraced house, all solid construction and in line with the Flemish EPB requirements of 2014. The results show that the environmental score of the buildings is comparable to benchmark values obtained based on the analysis of Belgian archetypes. Further, the building geometry and compactness are identified as key parameters, whereas the materialisation has a more limited influence on the environmental impact. Next research steps will focus on the modelling of more cases, including different construction types, energy performance levels, and potential impact mitigation strategies. The study concludes that the EPB buildings are promising to define environmental benchmarks for the Flemish dwelling stock.

Identifying Retrofit Technology to Improve Building Energy Performance: A Review

Identifying Retrofit Technology to Improve Building Energy Performance: A Review

Split-degree day method: A novel degree day method for improving building energy performance estimation

Degree days have been widely used in different applications in buildings, such as estimating building energy use and climate classification for building energy standards. However, there are limitations with the use of conventional degree days that result in inaccuracies in estimating building energy consumption using degree day-based methods. This paper proposes a new method, the split-degree day method, that shows substantially improved results in the accuracy of the building energy use estimation compared to the conventional degree day methods. The analysis in 801 locations in the U.S. using regression models showed that the new, proposed split-degree day method in this paper, compared to the conventional degree day method, better accounts for the weather parameters and more accurately estimates end-uses. The split-degree days showed an improvement of over 5% in the accuracy of the total annual energy use prediction, 8% for predicting the heating energy use, 0.3% for predicting the cooling energy use, and 33% for predicting the fan energy use. Additionally, the analysis showed improved results for the model with higher thermal mass and the model with a 24-hour operating schedule. These improvements were due to the fact that the split-degree day method includes more information related to the weather characteristics of a location, which is missing in a conventional degree day calculation procedure that relies on aggregated data at daily intervals. Furthermore, as opposed to the conventional degree days, the accuracy of the estimations using the split-degree days are shown to maintain their high accuracy when the base temperature deviates from the optimal base temperature. The proposed method can be used as a more accurate alternative to the conventional degree days in different applications, including but not limited to building climate classification, building energy estimation, and weather normalization for building energy savings calculation.

Energy assessment, economic analysis, and environmental study of an Iranian building: The effect of wall materials and climatic conditions

The selection of external wall materials based on climatic conditions is an effective way to improve building energy performance. Because of the wide variety of commercial materials available for exterior walls, engineers and building designers rarely select these materials based on climatic conditions. In this study, the thermal performance of a building was investigated using Design-Builder software (V4.2.0.054) in three different Iranian climatic zones (hot desert, cold semi-arid, and continental Mediterranean climates) and six common wall materials (AAC block, LECA block, extruded polystyrene). A novel thermoelectric device was used to measure the thermal conductivity of these materials. According to the findings, choosing the right wall materials based on climate conditions can reduce annual heating and cooling loads as well as carbon dioxide emissions by up to 23.2 %, 26.4 %, and 18.5 %, respectively. According to the economic analysis, the payback period time ranges between 1.7 and 17 years, depending on the climatic conditions and the type of exterior walls.

Building Occupancy Detection and Localization Using CCTV Camera and Deep Learning

Occupancy information plays a key role in analyzing and improving building energy performance. The advances of Internet of Things (IoT) technologies have engendered a shift in measuring building occupancy with IoT sensors, in which cameras in closed-circuit television (CCTV) systems can provide richer measurements. However, existing camera-based occupancy detection approaches cannot function well when scanning videos with a number of occupants and determining occupants’ locations. This article aims to develop a novel deep-learning-based approach for better building occupancy detection based on CCTV cameras. To do so, this research proposes a deep-learning model to detect the number of occupants and determine their locations in videos. This model consists of two main modules, namely, feature extraction and three-stage occupancy detection. The first module presents a deep convolutional neural network to perform residual and multibranch convolutional calculation to extract shallow and semantic features, and constructs feature pyramids through a bidirectional feature network. The second module performs a three-stage detection procedure with three sequential and homogeneous detectors which have increasing Intersection over Union (IoU) thresholds. Empirical experiments evaluate the detection performance of the approach with CCTV videos from a university building. Experimental results show that the approach achieves the superior detection performance when compared with baseline models.

Louver configuration comparison in three Canadian cities utilizing NSGA-II

Shading devices, particularly louvers, if chosen and designed correctly, reduce excessive uncontrolled solar gains and daylight in interior space, consequently improving building energy performance and occupants' visual comfort. However, various questions related to their adaptation to different climates, their designs, and optimized methods are unanswered. This paper aims to show that even in geographical areas with similar climate conditions, different louver designs are required.To this end, louver designs in three Canadian cities with almost similar climates are investigated. Objective functions are defined as minimizing energy use intensity and maximizing daylight use. Through a sensitivity analysis, louver parameters have been examined to determine which ones have a significant or insignificant effect on energy performance and visual comfort. Based on the results obtained from the sensitivity analysis, the proper range of each parameter was determined, and a metaheuristic algorithm was applied to reduce time and cost of optimization. Two algorithms (SPEA-2 and NSGA-II) are compared to understand which performs better in this problem. The results show that NSGA-II performs slightly better, and, using louvers in all case study cities positively affects visual comfort and energy performance. Second, louver depth and count are the most critical parameters in all three cities, and other parameters have a different effect based on the city latitude. The research contribution is to propose a workflow to integrate parametric design, sensitivity analysis, and NSGA-II to optimize louver design with various parameters and ranges. Also, the research proposes a guideline for designing louver in the three investigated cities.

Energy, economic and comfort optimization of building retrofits considering climate change: A simulation-based NSGA-III approach

Improving building energy performance is central to promote sustainability and mitigate climate change, but it has proved to be a challenging process requiring investigation of a large domain of energy retrofit measures and contrasting objectives. Besides, the unique characteristic of Iran, namely heavily subsidized energy prices and tiered utility rates, further complicate the problem. This research introduces a novel simulation-based multi-objective optimization approach based on a modification of NSGA-III algorithm, denoted as prNSGA-III, powered by parallel computing design and result-saving archive that offers an increased computational potential. The method is developed by integrating EnergyPlus as energy simulation engine with optimization algorithm coded in MATLAB programming language. A large domain of active, passive, water conservation, and renewable retrofit measures are explored to maximize environmental performance while minimizing thermal discomfort and life cycle cost. The method is applied to a residential building in Tehran, Iran, subjected to different scenarios with reference to future climate change and energy price variation. The results show that implementing suggested energy retrofit measures can yield substantial improvement in environmental performance and curb CO-eq emission by up to 73%, and also reduce thermal discomfort by up to 46%. However, energy-efficient solutions can only be achieved at the expense of increased life cycle cost. Moreover, the result demonstrates that climate change can affect optimal solutions, particularly heating and cooling systems, due to changes in external thermal loads.

Energy-Saving Potential Comparison of Different Photovoltaic Integrated Shading Devices (PVSDs) for Single-Story and Multi-Story Buildings

Building-integrated photovoltaic (BIPV) façades are a promising technique for improving building energy performance. This study develops energy simulation models of different photovoltaic-integrated shading devices (PVSDs) in single-story and multi-story office buildings. A cross-region study in China is carried out to explore the energy performance of PVSDs in five climate zones. The shading effect of the upper PVSDs is taken into account. The results show that (1) PVSDs can be applicable in hot and cold climates; shading effects lead to a notable difference in the optimal PVSDs style. The average comprehensive energy saving ratios of different PVSDs ranged from 16.12% (fixed PV louvres in the vertical plane) to 51.95% (lower single panel). The most rewarding PVSDs are for single-story buildings in Kunming and the least suitable are for multi-story buildings in Guangzhou. (2) In climate zones with little air-conditioning energy consumption, avoiding considerably increased lighting consumption by PVSDs is vital. (3) To reduce shading effects, solar panels with smaller widths or vertical placements can be adopted. In addition, the distance of the PV modules from the top edge of the windows is also critical. Building performance evaluation in the early design stage enables maximum benefits for the same input (total area of PV panels). The research methodology and data analysis presented can guide parameters design and the geographical applicability of PVSDs, providing a reference for optimal building energy performance.

Decarbonization scenarios for residential building sector in South Korea toward climate neutrality in 2050

ABSTRACT The decarbonisation strategy of the building sector, which accounts for 24% of national greenhouse gas (GHG) emissions, is important to achieve climate neutrality in Korea by 2050. In this study, a bottom-up model and four scenarios were developed for examining scenarios for the decarbonisation of the residential building by 2050. A comparative analysis of energy consumption, GHG emissions, and investment cost was performed. The result showed that building energy performance improvement and heating fuel electrification are key measures for decarbonisation. We concluded that an investment of KRW 6.2 trillion per year would be required to achieve the 2050 decarbonisation scenario. Policy strategies were proposed for increasing investment in accelerating building retrofit, mandating zero-energy building (ZEB), and prohibiting gas boilers.

Developing a hybrid time-series artificial intelligence model to forecast energy use in buildings

The development of a reliable energy use prediction model is still difficult due to the inherent complex pattern of energy use data. There are few studies developing a prediction model for the one-day-ahead energy use prediction in buildings and optimizing the hyperparameters of a prediction model is necessary. This study aimed to propose a hybrid artificial intelligence model for forecasting one-day ahead time-series energy consumption in buildings. The proposed model was developed based on the integration of the Seasonal Autoregressive integrated Moving average, the Firefly-inspired Optimization algorithm, and the support vector Regression (SAMFOR). A large dataset of energy consumption in 30-min intervals, temporal data, and weather data from six real-world buildings in Vietnam was used to train and test the model. Sensitivity analyses were performed to identify appropriate model inputs. Comparison results show that the SAMFOR model was more effective than the others such as the seasonal autoregressive integrated moving average (SARIMA) and support vector regression (SVR), SARIMA-SVR, and random forests (RF) models. Evaluation results on real-world building depicted that the proposed SAMFOR model achieved the highest accuracy with the root-mean-square error (RMSE) of 1.77 kWh in, mean absolute percentage error (MAPE) of 9.56%, and correlation coefficient (R) of 0.914. The comparison results confirmed that the SAMFOR model was effective for forecasting one-day-ahead energy consumption. The study contributes to (1) the knowledge domain by proposing the hybrid SAMFOR model for forecasting energy consumption in buildings; and (2) the state of practice by providing building managers or users with a powerful tool for analyzing and improving building energy performance.

Convolutional Neural Network Based Energy Consumption Management Model for the Full Life Cycle of Buildings and Information System Design

With the continuous improvement in China’s economy, the construction industry has developed and rampantly progressed. Besides the wastage of resources and energy, the development has caused serious pollution to the environment. This makes the construction industry a high energy-consuming and highly polluting industry. There is a pressing need to reduce the wastage of resources and to adequately manage consumption of energy throughout the life cycle of buildings. This paper explores an effective method of building life cycle energy management by appropriately utilizing information system and the emerging deep learning technology. To achieve energy saving in buildings, a feasible model is proposed for predicting, analyzing, and building energy consumption based on neural networks. By analyzing the massive data stored in the building information system, the operation of each subsystem in the building is guided and regulated to achieve energy deployment and build energy optimization. Focusing the key meters, the average generalization ability of the proposed model (R-Squared = 1.9, MSE = 1.02) is better than the other contemporarily used models, LightGBM, LSTM, and SVR. Moreover, the method can effectively predict the energy consumption of the whole life cycle of the building and has higher prediction accuracy. The method proposed has great significance in research related with improving building energy performance and designing decision support tool.