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A platform for research: civil engineering, architecture and urbanism
Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system
AbstractThere are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel’s daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy.
Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system
AbstractThere are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel’s daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy.
Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system
Li, Kangji (author) / Su, Hongye (author)
Energy and Buildings ; 42 ; 2070-2076
2010-06-20
7 pages
Article (Journal)
Electronic Resource
English
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