Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform

Zihan Chang, Yang Zhang and Wenbo Chen

Energy, 2019, vol. 187, issue C

Abstract: To a large extent, electricity price prediction is a daunting task because it depends on factors, such as weather, fuel, load and bidding strategies etc. Those features generate a lot of fluctuations to electricity price. As a type of RNN, LSTM has a good performance on processing time series data as well as some nonlinear and complex problems. To explore more accurate electricity price forecasting approach, in this paper, a new hybrid model based on wavelet transform and Adam optimized LSTM neural network, denoted as WT-Adam-LSTM, is proposed. After the wavelet transform, nonlinear sequence of electricity price can be decomposed and processed data will have a more stable variance, and the combination of Adam, one of efficient stochastic gradient-based optimizers, and LSTM can capture appropriate behaviors precisely for electricity price. This study presented four cases to verify the performance of the hybrid model, and the dataset from New South Wales of Australia and French were adopted to illustrate the excellence of the hybrid model. The results show that the proposed model can significantly improve the prediction accuracy.

Keywords: Component; Electricity price prediction; Wavelet transform; Long short-term memory; Adam (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (56)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:187:y:2019:i:c:s0360544219314768

DOI: 10.1016/j.energy.2019.07.134

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