This paper conducts research on the short-term electric load forecast method
under the background of big data. It builds a new electric load forecast model
based on Deep Auto-Encoder Networks (DAENs), which takes into account
multidimensional load-related data sets including historical load value,
temperature, day type, etc. A new distributed short-term load forecast method
based on TensorFlow and DAENs is therefore proposed, with an algorithm
flowchart designed. This method overcomes the shortcomings of traditional
neural network methods, such as over-fitting, slow convergence and local
optimum, etc. Case study results show that the proposed method has obvious
advantages in prediction accuracy, stability, and expansibility compared with
those based on traditional neural networks. Thus, this model can better meet
the demands of short-term electric load forecasting under big data scenario.
Description
Short-term Electric Load Forecasting Using TensorFlow and Deep Auto-Encoders
%0 Generic
%1 shi2019shortterm
%A Shi, Xin
%D 2019
%K forecasting tensorflow
%T Short-term Electric Load Forecasting Using TensorFlow and Deep
Auto-Encoders
%U http://arxiv.org/abs/1907.08941
%X This paper conducts research on the short-term electric load forecast method
under the background of big data. It builds a new electric load forecast model
based on Deep Auto-Encoder Networks (DAENs), which takes into account
multidimensional load-related data sets including historical load value,
temperature, day type, etc. A new distributed short-term load forecast method
based on TensorFlow and DAENs is therefore proposed, with an algorithm
flowchart designed. This method overcomes the shortcomings of traditional
neural network methods, such as over-fitting, slow convergence and local
optimum, etc. Case study results show that the proposed method has obvious
advantages in prediction accuracy, stability, and expansibility compared with
those based on traditional neural networks. Thus, this model can better meet
the demands of short-term electric load forecasting under big data scenario.
@misc{shi2019shortterm,
abstract = {This paper conducts research on the short-term electric load forecast method
under the background of big data. It builds a new electric load forecast model
based on Deep Auto-Encoder Networks (DAENs), which takes into account
multidimensional load-related data sets including historical load value,
temperature, day type, etc. A new distributed short-term load forecast method
based on TensorFlow and DAENs is therefore proposed, with an algorithm
flowchart designed. This method overcomes the shortcomings of traditional
neural network methods, such as over-fitting, slow convergence and local
optimum, etc. Case study results show that the proposed method has obvious
advantages in prediction accuracy, stability, and expansibility compared with
those based on traditional neural networks. Thus, this model can better meet
the demands of short-term electric load forecasting under big data scenario.},
added-at = {2020-08-27T13:06:12.000+0200},
author = {Shi, Xin},
biburl = {https://www.bibsonomy.org/bibtex/2aca1d9e0fc28ae18e01e9cd1103795f6/svejoe},
description = {Short-term Electric Load Forecasting Using TensorFlow and Deep Auto-Encoders},
interhash = {27debcb1997400a8d83de3540967498d},
intrahash = {aca1d9e0fc28ae18e01e9cd1103795f6},
keywords = {forecasting tensorflow},
note = {cite arxiv:1907.08941Comment: 8 pages},
timestamp = {2020-08-27T13:06:12.000+0200},
title = {Short-term Electric Load Forecasting Using TensorFlow and Deep
Auto-Encoders},
url = {http://arxiv.org/abs/1907.08941},
year = 2019
}