Open AccessLoad forecasting considering multiple influencing factors
Author(s) -
Xin Ning,
Liang Jin
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1449/1/012042
Subject(s) - data pre processing , computer science , normalization (sociology) , data mining , artificial neural network , electric power system , database normalization , preprocessor , field (mathematics) , scheduling (production processes) , missing data , process (computing) , artificial intelligence , machine learning , power (physics) , mathematical optimization , cluster analysis , physics , mathematics , quantum mechanics , sociology , anthropology , pure mathematics , operating system
With the improvement of power conservation awareness and the development of the power market, load forecasting is playing an important role gradually. Effective load forecasting can help power system operators to develop appropriate scheduling strategies and help users plan their power consumption rationally. Considering the important position of load forecasting in the future power system field, this paper focus on this field. Based on the measured data from Australia, this paper considers the environmental and social factors affecting the power consumption in the region. The LSSVM algorithm is used for short-term load forecasting. Firstly, the invalid data is eliminated by data preprocessing, the missing data is completed, and the data of the content is included. The normalization process is carried out, and then the processed data is used for prediction. Finally, the effectiveness of the Least squares support vector machine algorithm load prediction after considering various factors is verified by comparison with the traditional neural network algorithm.