Research into Short-Term Electricity Price Forecasting Utilizing Data Analysis
Authors: 
Yifei Wan, Hongpeng Qi, Fei Huang, Tong PanAuthors Info & Claims
Yifei Wan, Hongpeng Qi, Fei Huang, Tong PanAuthors Info & ClaimsPages 414 - 418
Abstract
The main objective of this study is to develop a data-driven methodology for making short-term electricity price forecasts, which will provide more reliable planning and decision-making support to various participants in the electricity market. Short-term electricity price forecasting is critical for electricity market participants as it helps them make informed decisions in a constantly fluctuating market environment. The methodology of this study consists of the following key steps: first, we employ a sample selection method based on grey correlation analysis (GCA) in order to efficiently remove useless samples with insignificant periodicity from the data, thereby reducing model complexity and improving computational efficiency. Second, we combine feature classification and temporal correlation features to carefully select the features with the strongest correlation with electricity price prediction using the GCA-based feature selection method, which further simplifies the model and reduces noise interference. Thirdly, we apply principal component analysis to reduce redundant information and noise in the dataset, which improves the quality and clarity of the data. Finally, we employ a differential evolutionary algorithm (DE) to optimise the support vector machine (SVM) model for electricity price prediction. This integrated approach enables electricity market participants to obtain timely and up-to-date information on electricity price forecasts, which helps them to make more accurate market transactions and decisions, reducing risks and improving benefits.
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Published In
November 2023
764 pages
ISBN:9798400708299
DOI:10.1145/3640115
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Publication History
Published: 26 March 2024
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ICITEE 2023
ICITEE 2023: 6th International Conference on Information Technologies and Electrical Engineering
November 3 - 5, 2023
Changde, Hunan, China
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