Based on CNN-LSTM for photovoltaic output prediction of multi-scale method
Year: 2024
Type: conference-paper
Abstract: To maximize power generating efficiency, accurate forecast of generation power is the very important job for the photovoltaic generation system. In this paper, photovoltaic production is forecasted using a understanding way called multi-scale based on convolutional neural network-long short-term memory network (CNN-LSTM) for photovoltaic output prediction of multi-scale method. However, existing methods are often challenged by the multi-scale nature of the data when dealing with PV output prediction, so a more efficient method is needed to overcome difficulties. Therefore, this paper put forward an idea of a multi-scale method based on CNN-LSTM, which combines CNN and LSTM to accurately predict photovoltaic output at different time scales. The CNN-LSTM model can achieve the precision of 0.9097 for 15 minutes, the prediction accuracy of 0.8750 for 1 hour, and the prediction accuracy of 0.7987 for 4 hours, which can prove that the model can meet the scheduling rules.
Authors: Hanlin Zhang, Zheng Tang, Yunyi Deng, Kai Wang
Institution: Qingdao University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402596810