Improved Transformer-PSO Short-Term Electricity Price Prediction Method Considering Multidimensional Influencing Factors
| Autor: | SUN Xin, WANG Simin, XIE Jingdong, JIANG Hailin, WANG Sen |
|---|---|
| Jazyk: | čínština |
| Rok vydání: | 2024 |
| Předmět: |
short-term electricity price prediction
multidimensional influencing factors autocorrelation analysis improved transformer model particle swarm optimization (pso) Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989 |
| Zdroj: | Shanghai Jiaotong Daxue xuebao, Vol 58, Iss 9, Pp 1420-1431 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1006-2467 |
| DOI: | 10.16183/j.cnki.jsjtu.2023.065 |
| Popis: | With the construction of a diversified electricity market, the factors affecting electricity prices are gradually increasing, and the market environment has undergone more drastic changes. In order to improve the accuracy of short-term electricity price prediction, an improved Transformer-particle swarm optimization (PSO) short-term electricity price prediction method considering multiple factors affecting electricity prices is proposed. First, based on the consideration of historical electricity prices and historical loads, the relevant factors of electricity price formation are further analyzed. The autocorrelation function is used to analyze the multi-cycle characteristics of electricity price and adjust input sequence, which overcomes the problem of limited prediction accuracy caused by using historical data only and adjusting the input sequence by experience. Then, by combining long short-term memory (LSTM), self-attention mechanism, multi-layer attention mechanism, and adopting a multi-input structure, an improved Transformer model is established to further enhance the ability of the LSTM model to capture long short-term dependencies between different time step information, to overcome the information utilization bottleneck of LSTM, and to adapt to complex multiple sequence inputs including historical electricity prices and various electricity price causes. In addition, the PSO intelligent algorithm is utilized to search for the optimal learning rate of the model at different learning stages, overcoming the limitations of manually adjusting the learning rate. Finally, the PJM market electricity price is used for example analysis. The results show that the proposed short-term electricity price prediction model can be applied to the market environment where electricity prices are affected by various factors and drastic changes, and effectively improve the accuracy of short-term electricity price prediction. |
| Databáze: | Directory of Open Access Journals |
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