Prophet-Based Medium and Long-Term Electricity Load Forecasting Research. Issue 1 (1st October 2022)

• Record Type: Journal Article
• Authors: Long, Cheng; Yu, Chengbo; Li, Tao
• Is Part Of: Journal of physics. Volume 2356:Issue 1(2022)
• Publication Date: 2022-10-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2356/1/012002
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract : The Prophet model decomposes the power load series of the region into trend terms, seasonal terms and holiday terms to visualize the data in the power load The model is then combined separately to achieve electricity load forecasting. The prediction results of the Prophet model were compared with those of the traditional ARIMA model and LSTM model, and the average absolute percentage error was 7.4342%, the root mean square error was 60376.8026MW, all of which were better than those of the ARIMA model and RNN model, which verified the effectiveness and feasibility of the model in power load forecasting.