Abstract
Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.
Highlights
The marketization of electricity is the product of the continuous development of the current electricity construct, electric energy is one of its most important segments, electricity price is an important factor in the electricity market, it could ensure stable operation of the market, and electricity price forecast has gradually become the focus of attention of scholars from different countries
Electricity price forecasting is of great significance in the electricity market
This paper aims at electricity price forecasting research with artificial intelligence methods
Summary
The marketization of electricity is the product of the continuous development of the current electricity construct, electric energy is one of its most important segments, electricity price is an important factor in the electricity market, it could ensure stable operation of the market, and electricity price forecast has gradually become the focus of attention of scholars from different countries. Amjady et al proposed day-ahead electricity price forecasting with the modified relief algorithm and hybrid neural network in reference [8] The effectiveness of this method was tested and compared against other methods in the Ontario, New England and Italian electricity markets, and has proven to be effective. Various methods have already been proposed for electricity price forecasting, with the development of deep learning technology, the performance of the hybrid structured deep neural network model proposed in this paper stands out in the various machine learning algorithms, the model most accurately predicts electricity price, such that power generators and consumers could make the appropriate decisions for power dispatch and usage.
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