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

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. • Long short-term Memory (LSTM) is applied for electricity price forecasting. • Adam is used as optimizer for LSTM. • Pauta criterion and Min-max normalization are used as preprocessing methods for price data. • Wavelet transform is used to decompose the electricity price series into a set of better-performing constitutive series. • Four cases proved that proposed WT-Adam-LSTM outperforms the existing models reported in the literature. [ABSTRACT FROM AUTHOR]