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Evolutionary model construction for electricity consumption prediction

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Abstract

Nowadays the ever-increasing energy consumption in buildings has caused supply shortages and adverse environmental impacts. The accurate prediction of energy consumption in smart buildings may help to monitor and control energy usage. As energy consumption is inevitably affected by exogenous factors such as temperature and wind speed, it is fundamentally important to select the informative channels of the factors, to extract the valuable features from the selected channels applied to the optimal-configured model, to improve prediction accuracy. However, existing work considers these parts in an almost disjoint way and lacks a model taking them into account, which may decrease prediction performance. Motivated by this challenge, an end-to-end prediction framework, called evolutionary model construction (EMC), is proposed to focus on performing these parts jointly. To implement EMC, a two-step evolutionary algorithm (EA) is designed, where one EA is firstly used to focus on exploiting the informative channels, while a new algorithm is proposed to concentrate on selecting the suitable feature extraction methods and respective time window sizes applied to the selected channels, and selecting the parameters in the predictor. The implementation of EMC chooses neural network with random weights as the predictor due to its highly recognized efficacy. We evaluate EMC in comparison with the existing approaches on a real-world electricity consumption dataset with various auxiliary factors. The superiority of EMC is further proved by analyzing and discussing the result according to the days in 1 week, time stamps in 1 day and month information on test samples.

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Acknowledgements

This work is supported by Buildings Engineered for Sustainability research Project, funded by RMIT Sustainable Urban Precincts Program.

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Authors and Affiliations

  1. Computer Science and Information Technology, School of Science, RMIT University, Melbourne, Australia

    Hui Song & Flora D. Salim

  2. Computer Science and Software Engineering, School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, Australia

    A. K. Qin

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  1. Hui Song
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Correspondence to Hui Song.

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Song, H., Qin, A.K. & Salim, F.D. Evolutionary model construction for electricity consumption prediction. Neural Comput & Applic 32, 12155–12172 (2020). https://doi.org/10.1007/s00521-019-04310-w

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