Short-term power load forecasting method based on Bagging-stochastic configuration networks

• Published in: PloS one Vol. 19; no. 3; p. e0300229
• Main Authors: Pang, Xinfu, Sun, Wei, Li, Haibo, Liu, Wei, Luan, Changfeng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.03.2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; China; Computer and Information Sciences; Earth Sciences; Electric power systems; Engineering and Technology; Forecasts and trends; Health aspects; Methods; Neural networks; Physical Sciences; Research and Analysis Methods; Supply and demand; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0300229

Accurate short-term load forecasting is of great significance in improving the dispatching efficiency of power grids, ensuring the safe and reliable operation of power grids, and guiding power systems to formulate reasonable production plans and reduce waste of resources. However, the traditional short-term load forecasting method has limited nonlinear mapping ability and weak generalization ability to unknown data, and it is prone to the loss of time series information, further suggesting that its forecasting accuracy can still be improved. This study presents a short-term power load forecasting method based on Bagging-stochastic configuration networks (SCNs). First, the missing values in the original data are filled with the average values. Second, the influencing factors, such as the weather- and week-type data, are coded. Then, combined with the data of influencing factors after coding, the Bagging-SCNs integration algorithm is used to predict the short-term load. Finally, by taking the daily load data of Quanzhou City, Zhejiang Province as an example, the program of the abovementioned method is compiled in Python language and then compared with the long short-term memory neural network algorithm and the single-SCNs algorithm. Simulation results show that the proposed method for medium- and short-term load forecasting has a high forecasting accuracy and a significant effect on improving the accuracy of load forecasting.