Analysis Of Building Energy Consumption Research Articles

A novel shadow calculation approach based on multithreaded parallel computing

Solar radiation significantly affects the thermal processes of buildings; thus, various building energy modeling programs have established corresponding shadow calculation modules. However, the large number of projection surfaces in a building requires extensive calculations, making it difficult to rapidly analyze the building shadow while ensuring hourly calculations. This study proposes an accelerated calculation framework for building shadows based on multithreaded parallel computing for an accurate building energy consumption analysis. Based on the basic principle of shadow projection calculation, the embedded, integrated development of a shadow calculation module is proposed, and a full-performance simulation platform kernel based on a multithreaded structure is constructed. Results show that the multithreaded shadow calculation framework can significantly shorten the calculation time. Based on case studies, a typical complex single building’s shadow calculation speed can be increased by nearly 2.4 times, whereas for city-scale building shadow simulations, it is only 5.4% of the original duration.

A framework for analyzing energy consumption in urban built-up areas based on single photonic radar and spatial big data

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide.

Simulation calculation and analysis of building energy consumption of multi-story glued laminated timber structures

As the only renewable building material, wood has a strong carbon sequestration effect. Thus, timber structures have the natural advantages of saving energy and reducing emissions. Currently, the research objects of building energy consumption are rarely timber structures. To further control the operational energy consumption of timber structures, this paper takes six-story glued laminated timber beam-column frame and light wood-frame shear wall structures as the research objects. Building energy consumption research is conducted through testing the heat transfer coefficient of the envelope structure, air circulation ratio, and Building Information Modeling (BIM) technology on-site. The results show that the energy consumption of the building is consistent with the current energy consumption of small- and medium-sized office buildings, and the heat gain and loss of the building are mainly due to solar radiation and heat conduction of the envelope, respectively. The airtightness of the building has the greatest influence on the energy consumption of the building, and the type of building structure and window-to-wall ratio have little influence on the energy consumption of the building.

Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions

The task of building energy prediction (BEP) is essential to several emerging research domains, including energy management, control optimization and fault detection. An accuracy prediction model contributes significantly to the improvement of building energy efficiency and flexibility. However, most existing prediction models are either based on single time series model or static model, making them perform poorly for long-term predictions. To solve this problem, this paper proposes a hybrid prediction model, which combines the deep ensemble (DE) model and autoregressive (AR) model together. Its motivation comes from the domain knowledge analysis of building energy consumption, where the energy consumption is decomposed into a global part and a local part. The deep ensemble model is adopted to predict its global part, and the AR model is employed to predict its local part. Comprehensive data experiments are conducted based on 50 real buildings across five building types to validate the model performance. The prediction horizon is the 24-h ahead sliding window prediction for one-year energy consumption. The results indicate that the hybrid prediction model outperforms the LSTM, DE-only, and ARIMA-only model, where its relative improvements of CV-RMSE are 28.7%, 35.98% and 18.47%, respectively. The experimental results also reveal that the types of buildings, i.e., office, lodging, will affect the model performance, which is attributed to their different user-behaviors. Based on the experimental results, it is demonstrated that the integration of static model and time series model is robust to the varying of building types and prediction steps, which should be the preferred choice of BEP task.

Research on Airflow distribution in Internet Data Center

With the rapid growth of IDC (Internet Data Center) construction, the energy consumption is also increasing gradually. It is extremely important to design a reasonable air distribution to reduce the energy consumption of IDC. The research object is an IDC Plant in Beijing. Airpak software is used to conduct DeST ( Building energy consumption analysis software) simulation of the air flow organization in IDC room. Under the same cooling load conditions, through the simulation of the three types of diffuse air supply, closed cold aisle and closed hot aisle in the IDC room, it is concluded that the air flow organization after the closed cold and hot aisle is excellent. The energy consumption analysis of the enclosure structure of the three air flow organizations of IDC room shows that the energy consumption of the closed cold aisle is lower than that of the closed hot aisle air-conditioning system.

Visual System Development for Construction Project Management by Using Machine Learning Algorithm

Building management involves multiple data sources related to the whole building, so the visualization system can display relevant data information intuitively and improve the overall management level. Based on this, with the help of machine learning algorithm, this paper designs a visualization system of building management, which effectively improves the efficiency of building management. Firstly, this paper uses graph neural network algorithm to extract and transform the data of buildings. Through the continuous iterative process of summing the hidden vectors of adjacent nodes, more information about the building drawing is obtained, and the loss function of graph neural network is defined. In the early stage of building management, especially in the management of new buildings, there are few data available in the system model. Therefore, this paper adopts deep learning algorithm, which can effectively realize the feature analysis and data domain mapping of the original data by generating confrontation network. Finally, a data set with similar distribution features is generated to make up for the shortage of the original data. In the visualization research of construction project management, the analysis of building energy consumption is one of the main functions. Because of the high complexity of building energy consumption data and the variety of data, the recognition accuracy of general machine learning algorithms is low, visualization system is feasible in the construction project management, and more intuitively shows the building energy consumption and other related information, thereby assisting the building managers to improve the safety of the building system.

Energy Consumption Analysis of Residential Building Using Autodesk Revit

Because of ongoing rise in global warming and other environmental factors, energy analysis for civil engineering structures are becomes an important factor in the advanced economic construction industry. Forecasting the energy usage of the building, employing appropriate energy-saving measures, and design for construction are all urgently required. The current work is focused on the capabilities of Building Information Modelling, such as Autodesk Revit/Insight Plug-in to perform energy consumption and energy cost analyses on a G+2 residential buildings to predict energy consumption of the building over its entire life span. The results demonstrated the influence of various building energy parameters to analyse optimised building energy requirements and reported reduction in energy consumption and energy cost of 31.3% and 30% respectively.

Impacts of Occupant Behavior on Building Energy Consumption and Energy Savings Analysis of Upgrading ASHRAE 90.1 Energy Efficiency Standards

Commercial prototype building models were developed by the United States Department of Energy (DOE) to analyze the energy savings of the ASHRAE 90.1 standard. However, in the DOE models, occupant behavior inputs were deterministic and the stochasticity of occupant behavior was not fully characterized. This study evaluated the impacts of stochastic occupant behavior on building energy consumption and energy savings analysis from upgrading the ASHRAE 90.1-2016 to 2019 in sixteen climate zones in the United States (U.S.). Three occupant behavior styles (austerity, normal, and wasteful) were defined to represent the different levels of energy consciousness. The DOE medium office prototype models were used as the baseline (normal behavior style). The occupant behavior functional mock-up unit (obFMU) was used to model the stochastic occupant behavior models (austerity and wasteful). The EnergyPlus medium office prototype models were modified to co-simulate with the obFMU models. The results of 16 climate zones were aggregated by the relative construction volume of each climate zone. The results showed that the weighted national annual source energy use intensity (EUI) of the austerity, normal, and wasteful behavior styles were 203.81 kWh/m2, 287.15 kWh/m2, and 388.33 kWh/m2 for ASHRAE 90.1-2016, and 192.43 kWh/m2, 273.48 kWh/m2, and 371.28 kWh/m2 for ASHRAE 90.1-2019, respectively. Compared to the normal behavior style, the austerity behavior style consumed 29% less source energy, while the wasteful behavior style consumed 36% more source energy. From upgrading the ASHRAE 90.1-2016 to 2019, the energy saving percentages based on the austerity, normal, and wasteful behavior styles were 5.59%, 4.78%, and 4.42%, respectively. The stochastic occupant behavior significantly impacted the building energy consumption, and their impacts on the energy savings analysis of upgrading ASHRAE 90.1 were also not negligible.

The values and barriers of Building Information Modeling (BIM) implementation combination evaluation in smart building energy and efficiency

With the continuous increase in the number of buildings, the phenomenon of excessively high energy consumption becomes more and more serious. It is particularly important to reduce the energy consumption during the operation of the whole life cycle of the building. Building Information Modeling (BIM) technology is based on the relevant parameters of construction projects, and uses digital information simulation to establish building information models. It is one of the technical means of building energy consumption analysis and energy saving management, that can be explained as the main reason for stakeholders to guide energy and efficiency changes of the driving factor in buildings. We start co-occurrence statistics and type analysis based on the co-occurrence context analysis, construct the co-occurrence information that affects the values and barriers combination of BIM implementation for smart building energy and efficiency, and chose the expert database of the Green Construction and Intelligent Building Branch (GCIBB) of China Construction Association, that obtained 104 samples through modified snowball sampling, using exploratory factor analysis with factor load linear functions, and combined factor variance contribution rate weights. We created index function of the values and barriers combination of BIM implementation in smart building energy and efficiency, and classified and calculated the comprehensive score of each factor according to the ranking evaluation index, and mapped its practical significance. The results show that the 18 combined evaluation indicators proposed in this paper are suitable for evaluating the three elements of benefit, efficiency and quality of smart buildings, and can achieve continuous optimization of building energy consumption. The constructed multi-factor combination evaluation model, the extracted common factor variance cumulative contribution rate is greater than 80%.

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings.

Prediction of building energy consumption is key to achieving energy efficiency and sustainability. Nowadays, the analysis or prediction of building energy consumption using building energy simulation tools facilitates the design and operation of energy-efficient buildings. The collection and generation of building data are essential components of machine learning models; however, there is still a lack of such data covering certain weather conditions. Such as those related to arid climate areas. This paper fills this identified gap with the creation of a new dataset for energy consumption of 3,840 records of typical residential buildings of the Saudi Arabia region of Qassim, and investigates the impact of residential buildings’ eight input variables (Building Size, Floor Height, Glazing Area, Wall Area, window to wall ratio (WWR), Win Glazing U-value, Roof U-value, and External Wall U-value) on the heating load (HL) and cooling load (CL) output variables. A number of classical and non-parametric statistical tools are used to uncover the most strongly associated input variables with each one of the output variables. Then, the machine learning Multiple linear regression (MLR) and Multilayer perceptron (MLP) methods are used to estimate HL and CL, and their results compared using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and coefficient of determination (R2) performance measures. The use of the IES simulation software on the new dataset concludes that MLP accurately estimates both HL and CL with low MAE, RMSE, and R2, which evidences the feasibility and accuracy of applying machine learning methods to estimate building energy consumption.

Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University, the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation (BP) neural network to solve nonlinear problems and have the ability of global approximation and generalization. By analyzing the influence of different uses, different building surfaces and different energy-saving schemes on the change of building energy consumption, the grey correlation method is used to determine the main influencing factors affecting each building energy consumption, including uses, building surfaces and energy-saving schemes, which are used as the input of the model and the building energy consumption as the output of the model, so as to establish the building energy consumption analysis model based on BP neural network. However, in practical application, BP neural network has the defects of slow convergence and easy to fall into local minima. In view of this, this paper uses genetic algorithm to optimize the weight and threshold of BP neural network, completes the improvement of various building energy consumption analysis models, and realizes the qualitative analysis of building energy consumption. The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm (GABP) in this paper is relatively high, which is more accurate than the results of the traditional BP neural network model, and the relative error of the analysis model is reduced from 11.56% to 8.13%, which proves that the GABP can be better suitable for the study of school building energy consumption analysis model, It is applied to the prediction of building energy consumption, which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.

Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning

Building energy consumption prediction plays an important role in realizing building energy conservation control. Limited by some external factors such as temperature, there are some problems in practical applications, such as complex operation and low prediction accuracy. Aiming at the problem of low prediction accuracy caused by poor timing of existing building energy consumption prediction methods, a building energy consumption prediction and analysis method based on the deep learning network is proposed in this paper. Before establishing the energy consumption prediction model, the building energy consumption data source is preprocessed and analyzed. Then, based on the Keras deep learning framework, an improved long short-term memory (ILSTM) prediction model is built to support the accurate analysis of the whole cycle of the prediction network. At the same time, the adaptive moment (Adam) estimation algorithm is used to update and optimize the weight parameters of the model to realize the adaptive and rapid update and matching of network parameters. The simulation experiment is based on the actual dataset collected by a university in Southwest China. The experimental results show that the evaluation indexes MAE and RMSE of the proposed method are 0.015 and 0.109, respectively, which are better than the comparison method. The simulation experiment proves that the proposed method is feasible.

Research on Energy Control System of Office Building Based on the BIM and BP Neural Network Technology

As the proportion of building energy consumption in the total energy consumption continues to increase, more and more countries pay attention to the prediction analysis and operation control of building energy consumption. In this paper, an office building energy management and control system based on the building information modeling (BIM) and back-propagation neural network (BPNN) technology is proposed. Firstly, the BPNN algorithm is used to predict the energy consumption of the building, and then the model predictive control (MPC) method is employed to search the optimal control parameters to realize the energy saving of air-conditioning system and other equipment. In addition, the office building environment model is established through the BIM technology, which can real-time display the operating conditions of equipment and more visually control the equipment in the office building. The experimental results show that the system can satisfy the comfort level and significantly reduce the energy consumption.

Long-Term Prediction of Weather for Analysis of Residential Building Energy Consumption in Australia

Globally, buildings account for nearly 40% of the total primary energy consumption and are responsible for 20% of the total greenhouse gas emissions. Energy consumption in buildings is increasing with the increasing world population and improving standards of living. Current global warming conditions will inevitably impact building energy consumption. To address this issue, this report conducted a comprehensive study of the impact of climate change on residential building energy consumption. Using the methodology of morphing, the weather files were constructed based on the typical meteorological year (TMY) data and predicted data generated from eight typical global climate models (GCMs) for three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) from 2020 to 2100. It was found that the most severe situation would occur in scenario RCP8.5, where the increase in temperature will reach 4.5 °C in eastern Australia from 2080–2099, which is 1 °C higher than that in other climate zones. With the construction of predicted weather files in 83 climate zones all across Australia, ten climate zones (cities)—ranging from heating-dominated to cooling-dominated regions—were selected as representative climate zones to illustrate the impact of climate change on heating and cooling energy consumption. The quantitative change in the energy requirements for space heating and cooling, along with the star rating, was simulated for two representative detached houses using the AccuRate software. It could be concluded that the RCP scenarios significantly affect the energy loads, which is consistent with changes in the ambient temperature. The heating load decreases for all climate zones, while the cooling load increases. Most regions in Australia will increase their energy consumption due to rising temperatures; however, the energy requirements of Adelaide and Perth would not change significantly, where the space heating and cooling loads are balanced due to decreasing heating and increasing cooling costs in most scenarios. The energy load in bigger houses will change more than that in smaller houses. Furthermore, Brisbane is the most sensitive region in terms of relative space energy changes, and Townsville appears to be the most sensitive area in terms of star rating change in this study. The impact of climate change on space building energy consumption in different climate zones should be considered in future design strategies due to the decades-long lifespans of Australian residential houses.

Analysis of feature matrix in machine learning algorithms to predict energy consumption of public buildings

With the development of building information and energy consumption data, machine learning methods are increasingly being used for predicting and analyzing building energy consumption. In this study, based on the actual energy consumption data of 2370 public buildings in Chongqing, we used six machine learning algorithms and recursive feature elimination to analyze the importance of each feature in the dataset. First, it is necessary to establish optimal prediction models for analyzing the importance of features, and XGboost has demonstrated its superiority in terms of accuracy and efficiency. Regardless of the algorithm, the cumulative contribution rate of the top ten features exceeds 80%, and there is an obvious diminishing marginal utility when the number of features continues to increase. The learning algorithms with similar kernels have similarities in judging feature importance. Tree model-based algorithms can achieve a satisfactory performance with fewer features compared to linear kernel-based algorithms. Furthermore, the dataset plays a crucial role in model performance. To achieve professional supervised learning, two conditions need to be considered simultaneously in data collection: the importance of features in physical processes and whether the samples have adequate variance on these features. Thus, this study can provide a reference for database establishment and big data analysis of urban building energy consumption.

시뮬레이션을 이용한 복합 단열재 내 PCM 두께에 따른 건물에너지소비량 분석

시뮬레이션을 이용한 복합 단열재 내 PCM 두께에 따른 건물에너지소비량 분석

Empirical analysis of building energy consumption and urban form in a large city: A case of Seoul, South Korea

In the climate change era, urban geometry serves to ensure sustainability for urban planners since it notably affects building energy consumption. However, previous studies have mainly conducted experimental investigations under hypothetical conditions. Without empirical evidence, it is difficult to implement these findings under real conditions. This paper examined empirical data to determine the urban geometry effect on building energy consumption in a large, complicated urban area. Urban geometric parameters, including the surface-to-volume ratio, obstruction angle, and building orientation, were measured across Seoul, and their correlations with building energy use were analyzed.The results show that the surface-to-volume ratio and obstruction angle are important, despite the empirical data complexity. Additional data analysis indicates that the heating energy type and building use consideration yields more reliable correlation coefficient trends. The potential impacts of natural ventilation and the urban heat island (UHI) effect on building energy consumption were examined. The UHI effect, rather than shadows on buildings, predominantly impacts the building electricity use in August and September. The correlation variation caused by the building permit date was investigated. The results in this paper could help urban planners and designers develop blueprints to control urban geometry and establish relevant design guidelines.

Energy Consumption Assessment and Energy-Saving Management in Tourist Resorts

For tourism industry, the ever-increasing energy consumption and the high carbon emissions are requiring close attention and prompt solution, thusly endowing the research on the energy-saving of buildings in tourist resorts very important and practical significance. For this reason, this study carefully considered the actual situations such the energy utilization method and the hygrothermal environment of tourist resorts and constructed a hygrothermal transfer model and the corresponding hygrothermal balance equation for buildings in tourist resorts; then, the paper proposed a few effective strategies for the energy-saving management of buildings in tourist resorts, and studied the annual energy consumption of tourist resorts and gave a building energy consumption analysis. At last, experimental results verified the rationality and effectiveness of the proposed energy consumption assessment and energy-saving management methods for tourist resort buildings in hygrothermal environment. This study provided a useful reference for the energy-saving methods of buildings in tourist resorts.

Simulation and Analysis of Building Energy Consumption in Port passenger Stations

Port passenger station buildings (PPSD) are an important part of transportation buildings in China, which is characterized by large human flow, long operating time, high load of equipment and lighting. The characteristics and functions of PPSD lead to the high energy consumption. However, the energy consumption analysis of PPSD was deficient. In this paper, the characteristics of energy consumption of port passenger stations in cold regions and hot summer and warm winter regions in China were analyzed. Based on eQUEST, the building models of port passenger stations are established. The influencing factors of the building energy consumption were analyzed through orthogonal experiment with SPSS. Results show that the factors such as summer indoor design temperature, heat source form, air conditioning form, window to wall ratio and lighting control mode are the key factors affecting the energy consumption of port passenger station.

Case analysis of energy consumption of the existing office building in the severe cold region

Abstract With the relevant requirements of energy-saving and green buildings put forward by our country, carrying out the building energy consumption analysis is the premise and foundation of implementing energy-saving improvement of the existing buildings. Nowadays, modern office buildings, whose energy consumption structure and proportion are relatively stable, are widely used. The energy review work of an existing office building in severe cold region is carried out. Investigation and statistics on the general situation of buildings, the general situation of energy consumption and water system consumption and the total energy consumption are implemented. Based on the audit, the energy consumption situation and characteristics in a certain year are analysed. According to the characteristics of energy-using structure, the energy-saving potential of buildings is analysed, and the reasonable energy-saving opinions and suggestions are proposed to provide theoretical support for the energy-saving management of office buildings in severe cold areas.