Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO
Rahmad Syah1, Mohammad Rezaei2, Marischa Elveny3
1Data Science & Computational Intelligence Research Group, Universitas Medan Area, Medan, Indonesia. rahmadsyah@staff.uma.ac.id.
Scientific Reports
|August 31, 2021
Summary
This study proposes an electricity price forecasting algorithm using Wavelet Packet Transform (WPT) and a novel Least Squares Support Vector Machine (LSSVM-SFK) with a modified HBMO for improved accuracy in electricity market predictions.
Area of Science:
- Energy Economics
- Computational Intelligence
- Signal Processing
Background:
- Accurate electricity price forecasting is crucial for market participants to optimize bidding strategies and enhance profitability.
- Existing forecasting methods often face challenges in handling the complex dynamics of electricity markets.
- The need for robust and efficient algorithms to predict future electricity prices is a significant research area.
Purpose of the Study:
- To develop a novel, powerful, and successful electricity price forecasting algorithm.
- To improve the accuracy and efficiency of electricity price prediction models.
- To enhance the profitability of market participants through better forecasting.
Main Methods:
- The proposed algorithm integrates Wavelet Packet Transform (WPT) for signal decomposition and Variational Mutual Information (VMI) for feature selection.
- A Least Squares Support Vector Machine with a Self-Adaptive Fuzzy Kernel (LSSVM-SFK) is employed for pattern extraction.
- A modified HBMO (Harris Hawks Optimization) algorithm is introduced for optimal tuning of LSSVM-SFK parameters.
Main Results:
- The algorithm successfully decomposes price signals into high and low-frequency subseries using WPT.
- VMI effectively selects valuable input data, reducing computational load.
- The LSSVM-SFK, optimized by the modified HBMO, demonstrates superior pattern extraction capabilities.
- Empirical evaluation on electricity markets shows the proposed algorithm achieves acceptable efficiency compared to other models.
Conclusions:
- The developed forecasting algorithm offers a significant improvement in electricity price prediction accuracy.
- The integration of WPT, VMI, LSSVM-SFK, and modified HBMO provides a robust framework for energy market analysis.
- The proposed method empowers market participants with reliable future price information for strategic decision-making.
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