Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria

Nantian Huang,Guowei Cai,Zhiqiang Hu,Dongfeng Yang

doi:10.3390/e18090330

Nantian Huang, Guowei Cai + Show 2 more

Open Access

https://doi.org/10.3390/e18090330

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Journal: Entropy	Publication Date: Sep 8, 2016
Citations: 28	License type: CC BY 4.0

Affiliation: Northeast Electric Power University

Abstract

A feature selection method based on the generalized minimum redundancy and maximum relevance (G-mRMR) is proposed to improve the accuracy of short-term load forecasting (STLF). First, mutual information is calculated to analyze the relations between the original features and the load sequence, as well as the redundancy among the original features. Second, a weighting factor selected by statistical experiments is used to balance the relevance and redundancy of features when using the G-mRMR. Third, each feature is ranked in a descending order according to its relevance and redundancy as computed by G-mRMR. A sequential forward selection method is utilized for choosing the optimal subset. Finally, a STLF predictor is constructed based on random forest with the obtained optimal subset. The effectiveness and improvement of the proposed method was tested with actual load data.

Highlights

A short-term load forecasting (STLF) predicts future electric loads with a particular prediction limit from one hour extending up to several days
Feature Selection Results Based on generalized minimum redundancy and maximum relevance (G-minimum-redundancy and maximum-relevance (mRMR)) and random forest (RF)
For the issues regarding the selection of reasonable features for STLF, a feature selection method based on G-mRMR and RF is proposed in this paper

Summary

Introduction

A short-term load forecasting (STLF) predicts future electric loads with a particular prediction limit from one hour extending up to several days. The primary target of smart grids, such as reducing the difference between peak and valley electric loads, large-scale renewable energy absorption, demand side response, and optimal economic operation of the power grid, needs accurate STLF results [1]. In the traditional methods, such as autoregressive integrated moving average (ARIMA) [3]. The combination of autoregressive and moving average in ARIMA is a better time series model for STLF [7]. According to the historical time-varying load data, the ARIMA is established and applied for predicting the forthcoming electrical load. The regression analysis uses historical data to establish simple but highly efficient regression models [8]. The Kalman filter improves the accuracy of STLF by estimating each component of load which is apportioned into random and fixed components

Methods

Results

Conclusion