LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method. (1st October 2022)

• Record Type: Journal Article
• Authors: Dai, Yeming; Wang, Yanxin; Leng, Mingming; Yang, Xinyu; Zhou, Qiong
• Is Part Of: Energy. Volume 256(2022)
• Publication Date: 2022-10-01
• Subjects: Photovoltaic power generation -- Prediction -- Locally weighted scatterplot smoothing -- Random forest -- Gated recurrent unit; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124661
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Accurate prediction of photovoltaic power generation is vital to guarantee smooth operation of power stations and ensure users' electricity consumption. As a good forecasting tool, Gated Recurrent Unit method has been widely used in different forecasting areas. However, the existing studies ignore the impact of data fluctuations on prediction accuracy, to fill the gaps and enhance prediction accuracy, several different data smoothing techniques are introduced and compared to reduce fluctuations, Random Forest method is used for feature selection, and RepeatVector layer extended by attribute dimensions and TimeDistributed layer with full connectivity are utilized to optimize the Gated Recurrent Unit model. A real-world case from the photovoltaic power plant in Xuhui District, Shanghai, China, is adopted to evaluate the performance of proposed method. The comparing results with Recurrent Neural Networks and Long Short-Term Memory, and the actual data as well, show that the proposed prediction method can effectively improve the prediction accuracy of photovoltaic power generation. We also use the daily and monthly data of The Desert Knowledge Australia Solar Centre in Australia to investigate whether the proposed method is suitable for short-term or medium and long-term prediction. The results indicate that our method is more appropriate for short-term prediction. Highlights: Introducing and comparing different data smoothing technologies to reduce the data fluctuation. Using Random Forest to extract the characteristics of natural factors. Adding RepeatVector layer and TimeDistributed layer into the GRU to improve prediction accuracy. Utilizing the dataset from Shanghai, China and three prediction models to verify the accuracy. Utilizing dataset from Alice Springs to verify that our method is more suitable for short-term forecasting.