Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM model

In order to formulate a reasonable power generation and transmission plan, and effectively prevent the waste of electricity resources, a power consumption prediction model based on wavelet transform and multi-layer LSTM is proposed. In this paper, the sample data is first denoised based on wavelet t...

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Main Author: Dianwei Chi
Format: Article
Language:English
Published: Elsevier 2022-07-01
Series:Energy Reports
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S235248472200169X
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author Dianwei Chi
author_facet Dianwei Chi
author_sort Dianwei Chi
collection DOAJ
description In order to formulate a reasonable power generation and transmission plan, and effectively prevent the waste of electricity resources, a power consumption prediction model based on wavelet transform and multi-layer LSTM is proposed. In this paper, the sample data is first denoised based on wavelet transform to eliminate the volatility of the electricity consumption data itself. Then, based on the pre-processed samples, the multi-layer LSTM model is used for training, and the proposed model is verified and predicted daily power consumption based on the power consumption of the area controlled by U.S. electric power company. The experimental results show that the prediction performance of this model is better than traditional LSTM and bidirectional LSTM. The mean square error is 0.019, and the coefficient of determination R2 is as high as 0.997. It also shows that wavelet denoising can further improve the prediction performance of the model.
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spelling doaj.art-7076a588205b49dd8168c88322cb61c12022-12-22T02:52:29ZengElsevierEnergy Reports2352-48472022-07-018220228Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM modelDianwei Chi0Yantai Institute of Technology, Yantai 264005, ChinaIn order to formulate a reasonable power generation and transmission plan, and effectively prevent the waste of electricity resources, a power consumption prediction model based on wavelet transform and multi-layer LSTM is proposed. In this paper, the sample data is first denoised based on wavelet transform to eliminate the volatility of the electricity consumption data itself. Then, based on the pre-processed samples, the multi-layer LSTM model is used for training, and the proposed model is verified and predicted daily power consumption based on the power consumption of the area controlled by U.S. electric power company. The experimental results show that the prediction performance of this model is better than traditional LSTM and bidirectional LSTM. The mean square error is 0.019, and the coefficient of determination R2 is as high as 0.997. It also shows that wavelet denoising can further improve the prediction performance of the model.http://www.sciencedirect.com/science/article/pii/S235248472200169XElectricity consumption predictionWavelet transformNoise reductionLSTM
spellingShingle Dianwei Chi
Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM model
Energy Reports
Electricity consumption prediction
Wavelet transform
Noise reduction
LSTM
title Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM model
title_full Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM model
title_fullStr Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM model
title_full_unstemmed Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM model
title_short Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM model
title_sort research on electricity consumption forecasting model based on wavelet transform and multi layer lstm model
topic Electricity consumption prediction
Wavelet transform
Noise reduction
LSTM
url http://www.sciencedirect.com/science/article/pii/S235248472200169X
work_keys_str_mv AT dianweichi researchonelectricityconsumptionforecastingmodelbasedonwavelettransformandmultilayerlstmmodel