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A Hybrid Day-ahead Electricity Price Forecasting Framework Based on Time Series by Xiaoping Xiong; Guohua Qing is a Engineering article available to read on EtoBox.
What is A Hybrid Day-ahead Electricity Price Forecasting Framework Based on Time Series about?
Electricity price forecasting (EPF) plays an indispensable role in the decision-making processes of electricity market participants. However, the complexity of electricity markets has made EPF increasingly difficult. Currently, popular methods for EPF are based on signal decomposition and suffer from computational redundancy and hyperparameter optimization challenges. In this paper, we propose a new hybrid forecasting framework to improve the forecasting accuracy of day-ahead electricity prices. The proposed model consists of three valuable strategies. First, an adaptive copula-based feature selection (ACBFS) algorithm based on the maximum correlation minimum redundancy criterion is proposed for selecting model input features. Second, a new method of signal decomposition technique for EPF field is proposed based on decomposition denoising strategy. Third, a Bayesian optimization and hyperband (BOHB) optimized long short-term memory (LSTM) model is used to improve the effect of hyperparameter settings on the prediction results. The effectiveness of the different techniques was broadly cross-validated using five datasets set up for the PJM electricity market, and the results indicate
Who reads A Hybrid Day-ahead Electricity Price Forecasting Framework Based on Time Series?
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- Author
- Xiaoping Xiong; Guohua Qing
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)