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Abstract: Presents the regularization procedure for the neural network reduction to obtain the best results of load forecasting in the power system.
A regularisation procedure for neural-network reduction in order to obtain the best results for load forecasting in a power system is presented.
Missing: Regularization | Show results with:Regularization
Abstract - The paper presents the regularization procedure for the neural network reduction to obtain the best results of load forecasting in the power ...
Abstract: A regularisation procedure for neural-network reduction in order to obtain the best results for load forecasting in a power system is presented.
Missing: Regularization | Show results with:Regularization
A regularisation procedure for neural-network reduction in order to obtain the best results for load forecasting in a power system is presented.
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Jun 14, 2024 · This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management
More accurate short-term load forecasting (STLF) technology is an effective way to improve the competitiveness of LSEs [3]- [5] . Specifically, STLF can help ...
Dropout is a popular regularization technique for deep neural net- works presented by Srivastava et al. [39].
The paper presents a methodology for the short-term commercial building electrical load forecasting through a regularized deep neural network.
Nov 13, 2020 · The aim of this work is to investigate the adequacy of neural network methodology for predicting the entire load curve day-ahead and evaluate its performance ...