A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning

The price of electricity is an important factor in the electricity market. Accurate electricity price forecasting (EPF) is very important to all competing electricity market parties. Decision-making in the electricity market is highly dependent on electricity prices, making an EPF model an important...

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Autores principales:	Xiaoming Xie, Meiping Li, Du Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electricity price forecasting (EPF) wavelet transform tensor fusion long short-term memory (LSTM) Technology T
Acceso en línea:	https://doaj.org/article/a69a4484e0ef42ea81783b532a16c31f
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spelling	oai:doaj.org-article:a69a4484e0ef42ea81783b532a16c31f2021-11-11T16:03:32ZA Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning10.3390/en142173331996-1073https://doaj.org/article/a69a4484e0ef42ea81783b532a16c31f2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7333https://doaj.org/toc/1996-1073The price of electricity is an important factor in the electricity market. Accurate electricity price forecasting (EPF) is very important to all competing electricity market parties. Decision-making in the electricity market is highly dependent on electricity prices, making an EPF model an important part of the orderly and efficient operation of the electricity market. Especially during the COVID-19 pandemic, the prices of raw materials for electricity production, such as coal, have risen sharply. Forecasting electricity prices has become particularly important. Currently, existing EPF prediction models face two main challenges: First, how to integrate multiscale electricity price data to obtain a higher prediction accuracy. Second, how to solve the problem of data noise caused by the fusion of EPF samples and multiscale data. To solve the above problems, we innovatively propose a tensor decomposition method to integrate multiscale electricity price data and use <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>L</mi><mn>1</mn></msub></mrow></semantics></math></inline-formula> regularization and wavelet transform to remove data noise. In general, this paper proposes a deep learning EPF prediction model, named the WT_TDLSTM model, based on tensor decomposition, a wavelet transform, and long short-term memory (LSTM). Among them, the LSTM method is used to predict electricity prices. We conducted experiments on three datasets. The experimental results of three data prove that the WT_TDLSTM model is better than the compared EPF model. The indicators of MSE and RMSE are 33.65–99.97% better than the comparison model. We believe that the WT_TDLSTM model is a good supplement to the EPF model.Xiaoming XieMeiping LiDu ZhangMDPI AGarticleelectricity price forecasting (EPF)wavelet transformtensor fusionlong short-term memory (LSTM)TechnologyTENEnergies, Vol 14, Iss 7333, p 7333 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electricity price forecasting (EPF) wavelet transform tensor fusion long short-term memory (LSTM) Technology T
spellingShingle	electricity price forecasting (EPF) wavelet transform tensor fusion long short-term memory (LSTM) Technology T Xiaoming Xie Meiping Li Du Zhang A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning
description	The price of electricity is an important factor in the electricity market. Accurate electricity price forecasting (EPF) is very important to all competing electricity market parties. Decision-making in the electricity market is highly dependent on electricity prices, making an EPF model an important part of the orderly and efficient operation of the electricity market. Especially during the COVID-19 pandemic, the prices of raw materials for electricity production, such as coal, have risen sharply. Forecasting electricity prices has become particularly important. Currently, existing EPF prediction models face two main challenges: First, how to integrate multiscale electricity price data to obtain a higher prediction accuracy. Second, how to solve the problem of data noise caused by the fusion of EPF samples and multiscale data. To solve the above problems, we innovatively propose a tensor decomposition method to integrate multiscale electricity price data and use <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>L</mi><mn>1</mn></msub></mrow></semantics></math></inline-formula> regularization and wavelet transform to remove data noise. In general, this paper proposes a deep learning EPF prediction model, named the WT_TDLSTM model, based on tensor decomposition, a wavelet transform, and long short-term memory (LSTM). Among them, the LSTM method is used to predict electricity prices. We conducted experiments on three datasets. The experimental results of three data prove that the WT_TDLSTM model is better than the compared EPF model. The indicators of MSE and RMSE are 33.65–99.97% better than the comparison model. We believe that the WT_TDLSTM model is a good supplement to the EPF model.
format	article
author	Xiaoming Xie Meiping Li Du Zhang
author_facet	Xiaoming Xie Meiping Li Du Zhang
author_sort	Xiaoming Xie
title	A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning
title_short	A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning
title_full	A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning
title_fullStr	A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning
title_full_unstemmed	A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning
title_sort	multiscale electricity price forecasting model based on tensor fusion and deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a69a4484e0ef42ea81783b532a16c31f
work_keys_str_mv	AT xiaomingxie amultiscaleelectricitypriceforecastingmodelbasedontensorfusionanddeeplearning AT meipingli amultiscaleelectricitypriceforecastingmodelbasedontensorfusionanddeeplearning AT duzhang amultiscaleelectricitypriceforecastingmodelbasedontensorfusionanddeeplearning AT xiaomingxie multiscaleelectricitypriceforecastingmodelbasedontensorfusionanddeeplearning AT meipingli multiscaleelectricitypriceforecastingmodelbasedontensorfusionanddeeplearning AT duzhang multiscaleelectricitypriceforecastingmodelbasedontensorfusionanddeeplearning
_version_	1718432432093396992

A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning

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