Abstract
Electricity price is an important indicator of the market operation. Accurate prediction of electricity price will facilitate the maximization of economic benefits and reduction of risks to the power market. At the same time, because of the excellent performance of deep learning models, using long-short term memory neural network (LSTM) and other deep learning models to predict time series has gradually become a research hotspot. In this paper, an optimized heterogeneous structure LSTM model is proposed to solve the problems of the single network structure and hyperparameter selection existing in the current research on LSTM. The heterogeneous structure LSTM is constructed based on the decomposed and reconstructed electricity price data, and sequence model-based optimization (SMBO) is used to optimize its hyperparameters. In order to verify the proposed model, online hourly forecasting and day-ahead hourly forecasting are tested on the electricity markets of Pennsylvania-New Jersey-Maryland (PJM). The experimental results show that the performance of the proposed model is much better than that of the general LSTM model and traditional models in accuracy and stability, providing a new idea for the use of LSTM for time series prediction.
Highlights
Electricity price is an important factor in the electricity market
Mean Absolute Percentage Error (MAPE) is improved by 47.3% compared with other traditional machine learning models [14]
A large number of studies have shown that because of the strong volatility and nonlinearity of electricity prices, if the similar time series are decomposed in multiple scales, and the prediction model is adjusted according to the different components with a different nonlinear degree, the prediction performance will be significantly improved compared with sole forecasting models [22]–[25]
Summary
Electricity price is an important factor in the electricity market. Accurate electricity price forecasting (EPF) is critical to all parties in the power market competition [1]. Two other neural network architectures, TD-CNN and C-LSTM, are used to model the load data based on different time dimensions to extract the information contained in the time series [16]. A large number of studies have shown that because of the strong volatility and nonlinearity of electricity prices, if the similar time series are decomposed in multiple scales, and the prediction model is adjusted according to the different components with a different nonlinear degree, the prediction performance will be significantly improved compared with sole forecasting models [22]–[25]. This paper proposes an optimized heterogeneous structure LSTM hybrid forecasting model for electricity price prediction. Improve the prediction performance of the forecasting model by designing appropriate LSTM structures for different components, adjusting the nonlinear degree of the network (Construction of heterogeneous structure).
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