Electricity Price Forecasting Model based on Gated Recurrent Units

TL;DR

This paper introduces a Gated Recurrent Units-based model for electricity price forecasting that incorporates load consumption data and an adaptive noise reduction technique, demonstrating effective real-world prediction performance.

Contribution

The paper presents a novel electricity price forecasting model combining GRUs with adaptive noise reduction and feature extraction using SAEs, improving prediction accuracy.

Findings

Effective prediction of electricity prices on real datasets.

Integration of noise reduction enhances model robustness.

Use of load consumption as an input improves forecast accuracy.

Abstract

The participation of consumers and producers in demand response programs has increased in smart grids, which reduces investment and operation costs of power systems. Also, with the advent of renewable energy sources, the electricity market is becoming more complex and unpredictable. To effectively implement demand response programs, forecasting the future price of electricity is very crucial for producers in the electricity market. Electricity prices are very volatile and change under the influence of various factors such as temperature, wind speed, rainfall, intensity of commercial and daily activities, etc. Therefore, considering the influencing factors as dependent variables can increase the accuracy of the forecast. In this paper, a model for electricity price forecasting is presented based on Gated Recurrent Units. The electrical load consumption is considered as an input variable in this model. Noise in electricity price seriously reduces the efficiency and effectiveness of analysis. Therefore, an adaptive noise reducer is integrated into the model for noise reduction. The SAEs are then used to extract features from the de-noised electricity price. Finally, the de-noised features are fed into the GRU to train predictor. Results on real dataset shows that the proposed methodology can perform effectively in prediction of electricity price.