Open AccessShort-term load forecasting model based on multi-model integration
Author(s) -
Hongwei Wang,
Yuansheng Huang,
Minjun Shi,
Shijian Liu
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 - 10.1088/1742-6596/1549/5/052007
Subject(s) - computer science , artificial neural network , extreme learning machine , term (time) , support vector machine , artificial intelligence , machine learning , process (computing) , data mining , predictive modelling , physics , quantum mechanics , operating system
Artificial intelligence and machine learning methods have gradually matured and have been widely used in short-term power load forecasting. In order to make better use of the advantages of different artificial intelligence prediction models and traditional prediction models and improve prediction accuracy, this paper proposes a short-term load prediction model based on multi-model stacking. Different from the combined prediction method, the model first uses three machine learning models, support vector machine (SVM), back propagation neural network (BPNN), and extreme learning machine (ELM) as the base learners, and uses different training data sets. Training is performed on the model, and then the prediction results of the three basic learners are used as the input of Gaussian Process Regression (GPR), and multiple models are integrated to obtain the final prediction result. In order to verify the effectiveness of the Stacking prediction model, this paper applies the short-term load data of the PJM market to this model. Compared with the three base learners, the prediction results show that the model can make full use of the advantages of different prediction models and effectively reduce Forecasting errors have practical significance for solving short-term load forecasting problems.