Journal
ENERGY AND BUILDINGS
Volume 43, Issue 10, Pages 2893-2899Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2011.07.010
Keywords
Genetic algorithm; ANFIS; Artificial Neural Networks; Hierarchical structure; Building energy prediction
Funding
- National Creative Research Groups Science Foundation of China (NCRGSFC) [60721062]
- National Natural Science Foundation of P.R. China [NSFC: 60736021]
- National High Technology Research and Development Program of China (863 Program) [2008AA042902]
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As a regular data-driven method, Artificial Neural Networks (ANNs) are popular in building energy prediction. In this paper, an alternative approach, namely, hybrid genetic algorithm-adaptive network-based fuzzy inference system (GA-ANFIS) is presented. In this model, GA optimizes the subtractive clustering's radiuses which help form the rule base, and ANFIS adjusts the premise and consequent parameters to optimize the forecasting performance. a hierarchical structure of ANFIS is also suggested to solve the probably curse-of-dimensionality problem. The performance of the proposed model is compared with ANN using two different data sets, which are collected from the Energy Prediction Shootout I contest and a library building located in Zhejiang University, China. Results show that the hybrid GA-ANFIS model has better performance than ANN in term of prediction accuracy. The proposed model also has the same scale of modeling time as ANN if parameters in GA procedure are carefully selected. It can be regarded as an alternative method in building energy prediction. (C) 2011 Elsevier B.V. All rights reserved.
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