Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm
Electricity price forecasting is a crucial aspect of spot trading in the electricity market and optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity price sequences often results in low accuracy in electricity price forecasting. To address this issue, this stu...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/13/5098 |
| _version_ | 1797591773541826560 |
|---|---|
| author | Laiqing Yan Zutai Yan Zhenwen Li Ning Ma Ran Li Jian Qin |
| author_facet | Laiqing Yan Zutai Yan Zhenwen Li Ning Ma Ran Li Jian Qin |
| author_sort | Laiqing Yan |
| collection | DOAJ |
| description | Electricity price forecasting is a crucial aspect of spot trading in the electricity market and optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity price sequences often results in low accuracy in electricity price forecasting. To address this issue, this study proposes a quadratic hybrid decomposition method based on ensemble empirical modal decomposition (EEMD) and wavelet packet decomposition (WPD), along with a deep extreme learning machine (DELM) optimized by a THPO algorithm to enhance the accuracy of electricity price prediction. To overcome the problem of the optimization algorithm falling into local optima, an improved optimization algorithm strategy is proposed to enhance the optimization-seeking ability of HPO. The electricity price series is decomposed into a series of components using EEMD decomposition and WPD decomposition, and the DELM model optimized by the THPO algorithm is built for each component separately. The predicted values of all the series are then superimposed to obtain the final electricity price prediction. The proposed prediction model is evaluated using electricity price data from an Australian electricity market. The results demonstrate that the proposed improved algorithm strategy significantly improves the convergence performance of the algorithm, and the proposed prediction model effectively enhances the accuracy and stability of electricity price prediction, as compared to several other prediction models. |
| first_indexed | 2024-03-11T01:42:07Z |
| format | Article |
| id | doaj.art-84f3f45023eb4c85b02d2197c89ddfa0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T01:42:07Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-84f3f45023eb4c85b02d2197c89ddfa02023-11-18T16:30:21ZengMDPI AGEnergies1996-10732023-07-011613509810.3390/en16135098Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO AlgorithmLaiqing Yan0Zutai Yan1Zhenwen Li2Ning Ma3Ran Li4Jian Qin5School of Electric Power, Civil Engineering and Architecture, Shanxi University, Taiyuan 030031, ChinaSchool of Electric Power, Civil Engineering and Architecture, Shanxi University, Taiyuan 030031, ChinaSchool of Electric Power, Civil Engineering and Architecture, Shanxi University, Taiyuan 030031, ChinaNorth China Electric Power Research Institute Co., Ltd., Beijing 100045, ChinaState Grid Taiyuan Electric Power Supply Company, Taiyuan 030000, ChinaState Grid Taiyuan Electric Power Supply Company, Taiyuan 030000, ChinaElectricity price forecasting is a crucial aspect of spot trading in the electricity market and optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity price sequences often results in low accuracy in electricity price forecasting. To address this issue, this study proposes a quadratic hybrid decomposition method based on ensemble empirical modal decomposition (EEMD) and wavelet packet decomposition (WPD), along with a deep extreme learning machine (DELM) optimized by a THPO algorithm to enhance the accuracy of electricity price prediction. To overcome the problem of the optimization algorithm falling into local optima, an improved optimization algorithm strategy is proposed to enhance the optimization-seeking ability of HPO. The electricity price series is decomposed into a series of components using EEMD decomposition and WPD decomposition, and the DELM model optimized by the THPO algorithm is built for each component separately. The predicted values of all the series are then superimposed to obtain the final electricity price prediction. The proposed prediction model is evaluated using electricity price data from an Australian electricity market. The results demonstrate that the proposed improved algorithm strategy significantly improves the convergence performance of the algorithm, and the proposed prediction model effectively enhances the accuracy and stability of electricity price prediction, as compared to several other prediction models.https://www.mdpi.com/1996-1073/16/13/5098hunter-prey optimizer algorithmensemble empirical mode decompositionquadratic hybrid decompositiondeep extreme learning machineelectricity price forecast |
| spellingShingle | Laiqing Yan Zutai Yan Zhenwen Li Ning Ma Ran Li Jian Qin Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm Energies hunter-prey optimizer algorithm ensemble empirical mode decomposition quadratic hybrid decomposition deep extreme learning machine electricity price forecast |
| title | Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm |
| title_full | Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm |
| title_fullStr | Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm |
| title_full_unstemmed | Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm |
| title_short | Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm |
| title_sort | electricity market price prediction based on quadratic hybrid decomposition and thpo algorithm |
| topic | hunter-prey optimizer algorithm ensemble empirical mode decomposition quadratic hybrid decomposition deep extreme learning machine electricity price forecast |
| url | https://www.mdpi.com/1996-1073/16/13/5098 |
| work_keys_str_mv | AT laiqingyan electricitymarketpricepredictionbasedonquadratichybriddecompositionandthpoalgorithm AT zutaiyan electricitymarketpricepredictionbasedonquadratichybriddecompositionandthpoalgorithm AT zhenwenli electricitymarketpricepredictionbasedonquadratichybriddecompositionandthpoalgorithm AT ningma electricitymarketpricepredictionbasedonquadratichybriddecompositionandthpoalgorithm AT ranli electricitymarketpricepredictionbasedonquadratichybriddecompositionandthpoalgorithm AT jianqin electricitymarketpricepredictionbasedonquadratichybriddecompositionandthpoalgorithm |