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4 SRM for Electric Load Forecasting
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 power load value.
Mar 15, 2019
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- ...
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 ...
In this paper we introduce the beneficial properties of applications of sparse representation and corresponding dictionary learning to the net load forecasting ...
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Jul 27, 2021 · The use of sparse representation methods for modeling and forecasting individual household electricity loads is studied in [18] by proposing an ...
This paper proposes a sparse transformer based approach for electricity load prediction. The layers of a transformer addresses the shortcomings of RNNs and ...
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Fangwan Huang, Xiangping Zheng, Zhiyong Yu , Guanyi Yang, Wenzhong Guo: Electric Load Forecasting Based on Sparse Representation Model. GPC 2018: 357-369.
Abstract: For mid-term or short-term electricity load forecasting， a feature extraction modeling method based on sparse representation is proposed．
Jul 19, 2021 · In this paper we introduce the beneficial properties of applications of sparse representation and corresponding dictionary learning to the net load forecasting ...
This study introduces a novel Sparse Dynamic Graph Neural Network (SDGNN) framework designed to address the complexities of forecasting heat load in district ...