An LSTM-Based Online Prediction Method for Building Electric Load During COVID-19

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Published Nov 3, 2020
Hao Tu Srdjan Lukic Abhishek Dubey Gabor Karsai

Abstract

Accurate prediction of electric load is critical to optimally controlling and operating buildings. It provides the opportunities to reduce building energy consumption and to implement advanced functionalities such as demand response in the context of smart grid. However, buildings are nonstationary and it is important to consider the underlying concept changes that will affect the load pattern. In this paper we present an online learning method for predicting building electric load during concept changes such as COVID-19. The proposed methods is based on online Long Short-Term Memory (LSTM) recurrent neural network. To speed up the learning process during concept changes and improve prediction accuracy, an ensemble of multiple models with different learning rates is used. The learning rates are updated in realtime to best adapt to the new concept while maintaining the learned information for the prediction.

How to Cite

Tu, H., Lukic, S., Dubey, A., & Karsai, G. (2020). An LSTM-Based Online Prediction Method for Building Electric Load During COVID-19. Annual Conference of the PHM Society, 12(1), 8. https://doi.org/10.36001/phmconf.2020.v12i1.1319
Abstract 119 | PDF Downloads 104

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Keywords

Machine learning, LSTM, Load prediction, Concept change, Learning rate, Adaptive, COVID-19

Section
Technical Papers

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