A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Building energy prediction using artificial neural networks: A literature survey
Highlights Conduct a comprehensive literature survey on building energy prediction using ANNs. Show the rising attention and research trend on ANNs in building energy prediction in the late five years. Introduce twelve ANN architectures applied in building energy prediction in detail. Discuss three open issues and challenges to investigate the future research directions.
Abstract Building Energy prediction has emerged as an active research area due to its potential in improving energy efficiency in building energy management systems. Essentially, building energy prediction belongs to the time series forecasting or regression problem, and data-driven methods have drawn more attention recently due to their powerful ability to model complex relationships without expert knowledge. Among those methods, artificial neural networks (ANNs) have proven to be one of the most suitable and potential approaches with the rapid development of deep learning. This survey focuses on the studies using ANNs for building energy prediction and provides a bibliometric analysis by selecting 324 related publications in the recent five years. This survey is the first review article to summarize the details and applications of twelve ANN architectures in building energy prediction. Moreover, we discuss three open issues and main challenges in building energy prediction using ANNs regarding choosing ANN architecture, improving prediction performance, and dealing with the lack of building energy data. This survey aims at giving researchers a comprehensive introduction to ANNs for building energy prediction and investigating the future research directions when they attempt to implement ANNs to predict building energy demand or consumption.
Building energy prediction using artificial neural networks: A literature survey
Highlights Conduct a comprehensive literature survey on building energy prediction using ANNs. Show the rising attention and research trend on ANNs in building energy prediction in the late five years. Introduce twelve ANN architectures applied in building energy prediction in detail. Discuss three open issues and challenges to investigate the future research directions.
Abstract Building Energy prediction has emerged as an active research area due to its potential in improving energy efficiency in building energy management systems. Essentially, building energy prediction belongs to the time series forecasting or regression problem, and data-driven methods have drawn more attention recently due to their powerful ability to model complex relationships without expert knowledge. Among those methods, artificial neural networks (ANNs) have proven to be one of the most suitable and potential approaches with the rapid development of deep learning. This survey focuses on the studies using ANNs for building energy prediction and provides a bibliometric analysis by selecting 324 related publications in the recent five years. This survey is the first review article to summarize the details and applications of twelve ANN architectures in building energy prediction. Moreover, we discuss three open issues and main challenges in building energy prediction using ANNs regarding choosing ANN architecture, improving prediction performance, and dealing with the lack of building energy data. This survey aims at giving researchers a comprehensive introduction to ANNs for building energy prediction and investigating the future research directions when they attempt to implement ANNs to predict building energy demand or consumption.
Building energy prediction using artificial neural networks: A literature survey
Lu, Chujie (author) / Li, Sihui (author) / Lu, Zhengjun (author)
Energy and Buildings ; 262
2021-11-20
Article (Journal)
Electronic Resource
English
ABC , Artificial bee colony , ANN , Artificial neural networks , ARIMA , Autoregressive integrated moving average , BA , Bat algorithm , CS , Cuckoo Search , DA , Dragonfly algorithm , DE , Differential evolution algorithm , EA , Evolutionary algorithm , ELM , Extreme learning machine , ENN , Elman neural networks , ESN , Echo state networks , FFNN , Feed forward neural networks , FFOA , Fruit fly optimization algorithm , GA , Genetic algorithm , GAN , Generative adversarial network , GPR , Gaussian process regression , GRU , Gated recurrent units , HVAC , Heating, ventilation, and air-conditioning , ICA , Imperialist competitive algorithm , IoT , Internet of Things , LSTM , Long short-term memory , MLP , Multilayer perceptron , MLR , Multiple linear regression , NARX , Nonlinear autoregressive neural network with exogenous inputs , PSO , Particle swarm optimization , RBF , Radial basis function , RBM , Restricted Boltzmann machine , RF , Random forest , RNN , Recurrent neural networks , SCOA , Sine cosine optimization algorithm , SOS , Symbiotic organism search , SVR , Support vector regression , TSBO , Teaching–learning-based optimization , WNN , Wavelet neural networks , Building energy prediction , Deep learning , Smart buildings
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