Authors | حسین شاهین زاده,حامد نفیسی,مهتاب باقری,سعیده مهربانی نجف آبادی,Francisco Jurado |
---|---|
Conference Title | 2025 6th International Conference on Optimizing Electrical Energy Consumption (OEEC) |
Holding Date of Conference | 2025-02-25 - 2025-02-26 |
Event Place | 1 - نجف آباد |
Presented by | دانشگاه آزاد اسلامی واحد نجف آباد |
Presentation | SPEECH |
Conference Level | International Conferences |
Abstract
Abstract— Accurate electricity price forecasting in competitive markets is crucial for both producers and consumers. Given the complexity and extreme volatility of electricity prices, predicting the occurrence and magnitude of price spikes is a significant challenge. This paper presents a novel hybrid strategy based on deep learning and the Grey Wolf Optimization (GWO) algorithm for forecasting both the occurrence and magnitude of electricity price spikes. In this approach, an initial feature analysis is conducted using the SHAP (SHapley Additive exPlanations) importance index to eliminate less influential features. Subsequently, a Convolutional Neural Network (CNN) is employed to extract complex features and identify spike patterns in time series data. The Recursive Feature Elimination (RFE) algorithm is applied to optimize input features. Finally, GWO is utilized to optimize the CNN weights for accurate spike magnitude prediction. The proposed method is evaluated using real-world electricity market data, and the results demonstrate its high accuracy in forecasting both the occurrence and magnitude of electricity price spikes.
tags: Keywords— Electricity Price Spike Forecasting; Deep Learning; SHAP Feature Importance Analysis; Recursive Feature Elimination (RFE); Convolutional Neural Network (CNN); Grey Wolf Optimization (GWO).