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2019 | OriginalPaper | Buchkapitel

Electric Load Forecasting Based on Sparse Representation Model

verfasst von : Fangwan Huang, Xiangping Zheng, Zhiyong Yu, Guanyi Yang, Wenzhong Guo

Erschienen in: Green, Pervasive, and Cloud Computing

Verlag: Springer International Publishing

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Abstract

Accurate electric load forecasting can prevent the waste of power resources and plays a crucial role in smart grid. The time series of electric load collected by smart meters are non-linear and non-stationary, which poses a great challenge to the traditional forecasting methods. In this paper, sparse representation model (SRM) is proposed as a novel approach to tackle this challenge. The main idea of SRM is to obtain sparse representation coefficients by the training set and the part of over-complete dictionary, and the rest part of over-complete dictionary multiplied with sparse representation coefficients can be used to predict the future load value. Experimental results demonstrate that SRM is capable of forecasting the complex electric load time series effectively. It outperforms some popular machine learning methods such as Neural Network, SVM, and Random Forest.

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Titel: Electric Load Forecasting Based on Sparse Representation Model
verfasst von: Fangwan Huang
Xiangping Zheng
Zhiyong Yu
Guanyi Yang
Wenzhong Guo
Verlag: Springer International Publishing
Buch: Green, Pervasive, and Cloud Computing
Print ISBN: 978-3-030-15092-1

Electronic ISBN: 978-3-030-15093-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-15093-8_26

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