A hybrid model based on bidirectional long short-term memory neural network and Catboost for short-term electricity spot price forecasting

Electricity price forecasting plays a crucial role in a liberalised electricity market. Generally speaking, long-term electricity price is widely utilised for investment profitability analysis, grid or transmission expansion planning, while medium-term forecasting is important to markets that involve medium-term contracts. Typical applications of medium-term forecasting are risk management, balance sheet calculation, derivative pricing, and bilateral contracting. Short-term electricity price forecasting is essential for market providers to adjust the schedule of production, i.e., balancing consumers' demands and electricity generation. Results from short-term forecasting are utilised by market players to decide the timing of purchasing or selling to maximise profits. Among existing forecasting approaches, neural networks are regarded as the state of art method due to their capability of modelling high non-linearity and complex patterns inside time series data. However, deep neural networks are not studied comprehensively in this field, which represents a good motivation to fill this research gap. In this article, a deep neural network-based hybrid approach is proposed for short-term electricity price forecasting. To be more specific, categorical boosting (Catboost) algorithm is used for feature selection and a bidirectional long short-term memory neural network (BDLSTM) will serve as the main forecasting engine in the proposed method. To evaluate the effectiveness of the proposed approach, 2018 hourly electricity price data from the Nord Pool market are invoked as a case study. Moreover, the performance of the proposed approach is compared with those of multi-layer perception (MLP) neural network, support vector regression (SVR), ensemble tree, ARIMA as well as two recent deep learning-based models, gated recurrent units (GRU) and LSTM models. A real-world dataset of Nord Pool market is used in this study to validate the proposed approach. Mean percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) are used to measure the model performance. Experiment results show that the proposed model achieves lower forecasting errors than other models considered in this study although the proposed model is more time consuming in terms of training and forecasting.