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Your search keyword '"Hu, Chenglei"' showing total 2 results
Start Over Author "Hu, Chenglei" Topic genetic algorithm
2 results on '"Hu, Chenglei"'

1. Forecasting building energy consumption based on hybrid PSO-ANN prediction model

Author

Liu Guohai, Li Kangji, Hu Chenglei, and Pan Lei

Subjects
Engineering, Artificial neural network, business.industry, Computer Science::Neural and Evolutionary Computation, Particle swarm optimization, Machine learning, computer.software_genre, Field (computer science), Data-driven, ComputingMethodologies_PATTERNRECOGNITION, Dimension (vector space), Principal component analysis, Genetic algorithm, Artificial intelligence, business, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), computer, Energy (signal processing)
Abstract

As a popular data driven method, Artificial Neural Networks (ANNs) are widely applied in building energy prediction field. In this paper, a hybrid prediction approach that combines Particle Swarm Optimization (PSO) and ANN is presented. Before the prediction model applied, the principal component analysis (PCA) is used for the selection of the input variables, which helps to reduce the input dimension and simplify the model structure. To improve the prediction accuracy, PSO is used to adjust the ANN model's weight and threshold values. The performance of the proposed hybrid model is investigated using the data set of the Energy Prediction Shootout I contest, and the results indicate that PSO-ANN have better performance than regular ANN in term of prediction accuracy. In addition, another kind of hybrid prediction model which combines Genetic Algorithm (GA) and ANN is also proposed. Performance comparison shows that PSO-ANN has the same accuracy level with GA-ANN, and has simpler structure which is more suitable for online prediction tasks.

Published
2015

2. Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis.

Author

Li, Kangji, Hu, Chenglei, Liu, Guohai, and Xue, Wenping

Subjects
*ENERGY consumption of buildings, *BUILDING performance, *PREDICTION models, *ARTIFICIAL neural networks, *PRINCIPAL components analysis, *GENETIC algorithms
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. [ABSTRACT FROM AUTHOR]

Published
2015
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