Short-term load forecasting based on multivariate linear regression

With the rapid development of micro grid, the power load forecast is important in system. Short-term load forecasting (STLF) plays an important role in the overall operation efficiency of micro grid. In order to improve the accuracy of STLF, this paper proposes a combined model, which is multivariate linear regression(Multi-LR) with multi-label based on K-nearest neighbor (K-NN) and K-means. We use multi-label and K-NN algorithm to give different weight of each cluster for the forecasting points and build models by Multi-LR. In this paper, the test data which include daily temperature (which include highest temperature and lowest temperature) and power load of a quarter of an hour from a community compared with the results only using Multi-LR to forecast power load, it is concluded that the combined model can achieve high accuracy and reduce the running time.

[1]  Jie Wu,et al.  Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model , 2013 .

[2]  Abbas Khosravi,et al.  Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Abdul Hanan Abdullah,et al.  Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search , 2014 .

[4]  Shashank Mishra,et al.  Short Term Load Forecasting Using ANN and Multiple Linear Regression , 2016, 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT).

[5]  Dong Yue,et al.  Short-Term Load Forecasting Model Based on Multi-label and BPNN , 2017, LSMS/ICSEE.

[6]  Michael T. Manry,et al.  Comparison of very short-term load forecasting techniques , 1996 .

[7]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[8]  G. Jang,et al.  Short-term load forecasting for the holidays using fuzzy linear regression method , 2005, IEEE Transactions on Power Systems.

[9]  Jerzy Stefanowski,et al.  Experiments on Solving Multiclass Learning Problems by n2-classifier , 1998, ECML.

[10]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[11]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..