Research on electricity price prediction based on deep learning models

• Main Author: Chen, Ran
• Format: Conference Proceeding
• Language: English
• Published: SPIE 15.03.2024
• ISBN: 9781510674684; 1510674683
• ISSN: 0277-786X
• DOI: 10.1117/12.3026904

Electricity price prediction plays an important role in energy management and decision making. In this study, we use a deep learning model, specifically Long Short-Term Memory Network (LSTM), to predict electricity price data from different cities. We first pre-process the data, including data normalization and outlier processing, to improve the performance of the model. Subsequently, we constructed LSTM models with multi-layer structure for electricity price prediction in each city. In addition, in order to take into account the differences between cities, we introduced electricity load weights to more accurately synthesize the prediction results for each city. Finally, by applying the decision level fusion algorithm, the results show that the deep learning model performs well in electricity price prediction and provides strong support for energy management and policy making.