Artificial intelligence-based power market price prediction in smart renewable energy systems: Combining prophet and transformer models

• Published in: Heliyon 2024-10, Vol.10 (20), p.e38227, Article e38227
• Main Authors: Huang, Cheng, Zhao, Tianhui, Huang, Di, Cen, Bingcheng, Zhou, Qian, Chen, Wenjia
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Data analysis; Deep learning; Electricity market; Multi-source data; Price forecasting; Renewable energy systems
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e38227

With the increasing integration of smart renewable energy systems and power electronic converters, electricity market price prediction is particularly important. It is not only crucial for the interests of power suppliers and market regulators but also plays a key role in ensuring the reliable and flexible operation of the power system, particularly during extreme weather events or abnormal conditions. This study develops a hybrid time series forecasting model that combines Prophet and Transformer, which takes advantage of deep learning to provide a new solution for electricity market price forecasting. By introducing the Stacking optimization strategy, this study improves the accuracy and stability of electricity market price sequence prediction. In addition, the study tries to integrate traditional time series forecasting methods (such as the Prophet model) with deep learning models (such as the Transformer model), aiming to make full use of their respective advantages to achieve more accurate and stable predictions. Through experimental evaluation on four electricity market data sets, this study finds that the hybrid forecast model exhibits significant performance improvements in enhancing the accuracy and stability of electricity market price predictions. This method not only provides a more accurate tool for power market price prediction, but also provides solid technical support for the efficient operation and sustainable development of smart renewable energy systems. Experimental results also show that the combination of deep learning models with traditional time series methods and the introduction of Stacking strategies is crucial to improving the performance of power market price prediction, and also helps us better understand and design smart renewable energy systems, price and energy management strategies, thereby providing an effective method for achieving efficient and reliable power and energy transmission.