Journal ArticleDOI
A review on the prediction of building energy consumption
Hai‐Xiang Zhao,Frédéric Magoulès +1 more
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In this paper, the authors present a review of recent developed models for predicting building energy consumption, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods, and further prospects are proposed for additional research reference.Abstract:
The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.read more
Citations
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Journal ArticleDOI
A review of data-driven building energy consumption prediction studies
Kadir Amasyali,Nora El-Gohary +1 more
TL;DR: A review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation is provided in this paper.
Journal ArticleDOI
A review on applications of ANN and SVM for building electrical energy consumption forecasting
Ahmad Sukri Ahmad,Mohammad Yusri Hassan,M. P. Abdullah,Hasimah Abdul Rahman,Faridah Hussin,Huda Abdullah,Rahman Saidur +6 more
TL;DR: This paper reviews the building electrical energy forecasting method using artificial intelligence (AI) methods such as support vector machine (SVM) and artificial neural networks (ANN), regarding the potential of hybrid method of Group Method of Data Handling and Least Square Support Vector Machine (LSSVM), or known as GLSSVM, to forecastBuilding electrical energy consumption.
Journal ArticleDOI
State of the art in building modelling and energy performances prediction: A review
TL;DR: A detailed review and discussion of these works can be found in this article, where the authors present the main machine learning tools used for prediction of energy consumption, heating/cooling demand, indoor temperature.
Journal ArticleDOI
A review on time series forecasting techniques for building energy consumption
TL;DR: The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building and the nine most popular forecasting techniques based on the machine learning platform are analyzed.
Journal ArticleDOI
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †
TL;DR: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.
References
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Journal ArticleDOI
EnergyPlus: creating a new-generation building energy simulation program
Drury B. Crawley,Linda K. Lawrie,F.C. Winkelmann,Walter F. Buhl,Y.Joe Huang,C.O. Pedersen,Richard Karl Strand,R.J. Liesen,Daniel E. Fisher,Michael J. Witte,Jason Glazer +10 more
TL;DR: EnergyPlus as discussed by the authors is a building energy simulation tool that includes a number of innovative simulation features such as variable time steps, user-configurable modular systems that are integrated with a heat and mass balance-based zone simulation, and input and output data structures tailored to facilitate third party module and interface development.
Journal ArticleDOI
Contrasting the capabilities of building energy performance simulation programs
TL;DR: In this paper, a comparison of the features and capabilities of twenty major building energy simulation programs is presented, based on information provided by the program developers in the following categories: general modeling features; zone loads; building envelope and daylighting and solar; infiltration, ventilation and multizone airflow; renewable energy systems; electrical systems and equipment; HVAC systems; HVC equipment; environmental emissions; economic evaluation; climate data availability, results reporting; validation; and user interface, links to other programs, and availability.
Book
Energy Simulation in Building Design
TL;DR: In this article, the authors present a comprehensive overview of the latest developments in energy simulation in building design, including combined thermal/lighting and CFD simulation, advanced glazings, indoor air quality and photovoltaic components.
Book
Heating, Ventilating, and Air Conditioning: Analysis and Design
TL;DR: In this article, the authors present a detailed discussion of common HVAC units and their dimensions, as well as the basic concerns of IAQ, such as comfort, health, and environment.
Journal ArticleDOI
Applying support vector machines to predict building energy consumption in tropical region
TL;DR: In this article, support vector machines (SVM) were used to forecast building energy consumption in the tropical region, and the performance of SVM with respect to two parameters, C and ǫ, was explored using stepwise searching method based on radial-basis function (RBF) kernel.