期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 92, 期 -, 页码 380-389出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2017.09.059
关键词
Energy efficiency; Neural networks; Time series forecasting; Evolutionary algorithm
类别
资金
- Department of Computer Science and Artificial Intelligence of the University of Granada [TIN201564776-C3-1-R]
Buildings are an essential part of our social life. People spend a substantial fraction of their time and spend a high amount of energy in them. There is a grand variety of systems and services related to buildings, in order to better control and monitoring, The prompt taking of decisions may prevent costs and contamination. This paper proposes a method for energy consumption forecasting in public buildings, and thus, achieve energy savings, in order to improve the energy efficiency, without affecting the comfort and wellness. The prediction of the energy consumption is indispensable for the intelligent systems operations and planning. We propose an Elman neural network for forecasting such consumption and we use a genetic algorithm to optimize the weight of the models. This paper concludes that the proposed method optimizes the energy consumption forecasting and improves results attained in previous studies. (C) 2017 Elsevier Ltd. All rights reserved.
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