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A platform for research: civil engineering, architecture and urbanism
Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis
Highlights Present a hybrid iPSO-ANN prediction model for short term building electricity consumption forecasting. An improved PSO algorithm is applied to adjust ANN structure's weights and threshold values. PCA method is used for the selection of the input variables, which helps to reduce the input dimension. Better performances are obtained compared with regular ANN and GA-ANN models using two different data sets.
Abstract As a popular data driven method, artificial neural networks (ANNs) have been widely applied in building energy prediction field for decades. To improve the short term prediction accuracy, this paper presents a kind of optimized ANN model for hourly prediction of building electricity consumption. An improved Particle Swarm Optimization algorithm (iPSO) is applied to adjust ANN structure's weights and threshold values. The principal component analysis (PCA) is used to select the significant modeling inputs and simplify the model structure. The investigation utilizes two different historical data sets in hourly interval, which are collected from the Energy Prediction Shootout contest I and a campus building located in East China. For performance comparison, another two prediction models, ANN model and hybrid Genetic Algorithm-ANN (GA-ANN) model are also constructed in this study. The comparison results reveal that both iPSO-ANN and GA-ANN models have better accuracy than that of ANN ones. From the perspective of time consuming, the iPSO-ANN model has shorter modeling time than GA-ANN method. The proposed prediction model can be thought as an alternative technique for online prediction tasks of building electricity consumption.
Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis
Highlights Present a hybrid iPSO-ANN prediction model for short term building electricity consumption forecasting. An improved PSO algorithm is applied to adjust ANN structure's weights and threshold values. PCA method is used for the selection of the input variables, which helps to reduce the input dimension. Better performances are obtained compared with regular ANN and GA-ANN models using two different data sets.
Abstract As a popular data driven method, artificial neural networks (ANNs) have been widely applied in building energy prediction field for decades. To improve the short term prediction accuracy, this paper presents a kind of optimized ANN model for hourly prediction of building electricity consumption. An improved Particle Swarm Optimization algorithm (iPSO) is applied to adjust ANN structure's weights and threshold values. The principal component analysis (PCA) is used to select the significant modeling inputs and simplify the model structure. The investigation utilizes two different historical data sets in hourly interval, which are collected from the Energy Prediction Shootout contest I and a campus building located in East China. For performance comparison, another two prediction models, ANN model and hybrid Genetic Algorithm-ANN (GA-ANN) model are also constructed in this study. The comparison results reveal that both iPSO-ANN and GA-ANN models have better accuracy than that of ANN ones. From the perspective of time consuming, the iPSO-ANN model has shorter modeling time than GA-ANN method. The proposed prediction model can be thought as an alternative technique for online prediction tasks of building electricity consumption.
Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis
Li, Kangji (author) / Hu, Chenglei (author) / Liu, Guohai (author) / Xue, Wenping (author)
Energy and Buildings ; 108 ; 106-113
2015-09-01
8 pages
Article (Journal)
Electronic Resource
English
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