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14. Prediction of building energy consumption: A comparative study

Author

Zixiang Ding, Chengdong Li, Guiqing Zhang, and Xu Liwen

Subjects
Consumption (economics), Adaptive neuro fuzzy inference system, Artificial neural network, Computer science, business.industry, 020209 energy, 02 engineering and technology, Energy consumption, Machine learning, computer.software_genre, Support vector machine, 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, Time series, business, computer, Selection (genetic algorithm), Extreme learning machine
Abstract

This paper evaluates the performance of four artificial intelligence algorithms for building energy consumption prediction. The backward propagation neural network (BPNN), support vector regression (SVR), adaptive network-based fuzzy inference system (ANFIS) and extreme learning machine (ELM) methods are reviewed and their performances for predicting building energy consumption are compared. A selection of 12 cases with different numbers of input-variables is used to test each technique's performance. Three indices, training and testing root mean squared errors (RMSEs), and modeling training time are chosen as the criterions for performance evaluation. The experimental results indicate that the ELM is the best one for building energy consumption prediction when all the three indices are considered.

Published
2017

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