Demand-Aware Electricity Price Prediction Based on LSTM and Wavelet Transform

Koki Iwabuchi; Kenshiro Kato; Daichi Watari; Ittetsu Taniguchi; Francky Catthoor; Elham Shirazi; Takao Onoye

doi:10.4229/EUPVSEC20212021-7DV.1.4

Demand-Aware Electricity Price Prediction Based on LSTM and Wavelet Transform

Koki Iwabuchi, Kenshiro Kato, Daichi Watari, Ittetsu Taniguchi, Francky Catthoor, Elham Shirazi, Takao Onoye

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

This paper proposes a novel electricity price prediction method using past electricity demand. The previous method predicts the electricity price using LSTM after wavelet transform of the past electricity price series. Wavelet transform decomposes the series into more smooth and stable series, and accurate prediction is performed by LSTM with these series. The proposed method also uses the electricity demand series, and the demand is also applied wavelet transform for effective LSTM step. Experimental results show the effectiveness of the proposed method, and the accuracy is drastically improved over the previous method.

Original language	English
Title of host publication	EU PVSEC 2023
Pages	1668-1671
Number of pages	4
DOIs	https://doi.org/10.4229/EUPVSEC20212021-7DV.1.4
Publication status	Published - 2021
Event	38th European Photovoltaic Solar Energy Conference and Exhibition, EU PVSEC 2021 - online Duration: 6 Sept 2021 → 10 Sept 2021 Conference number: 38

Publication series

Name	38th European Photovoltaic Solar Energy Conference and Exhibition; 1668-1670

Conference

Conference	38th European Photovoltaic Solar Energy Conference and Exhibition, EU PVSEC 2021
Abbreviated title	PVSEC 2021
Period	6/09/21 → 10/09/21

Keywords

NLA

Access to Document

10.4229/EUPVSEC20212021-7DV.1.4

Cite this

@inproceedings{d35fd20092d6420091a4b4d7c82324a1,

title = "Demand-Aware Electricity Price Prediction Based on LSTM and Wavelet Transform",

abstract = "This paper proposes a novel electricity price prediction method using past electricity demand. The previous method predicts the electricity price using LSTM after wavelet transform of the past electricity price series. Wavelet transform decomposes the series into more smooth and stable series, and accurate prediction is performed by LSTM with these series. The proposed method also uses the electricity demand series, and the demand is also applied wavelet transform for effective LSTM step. Experimental results show the effectiveness of the proposed method, and the accuracy is drastically improved over the previous method.",

keywords = "NLA",

author = "Koki Iwabuchi and Kenshiro Kato and Daichi Watari and Ittetsu Taniguchi and Francky Catthoor and Elham Shirazi and Takao Onoye",

year = "2021",

doi = "10.4229/EUPVSEC20212021-7DV.1.4",

language = "English",

isbn = "3-936338-78-7",

series = "38th European Photovoltaic Solar Energy Conference and Exhibition; 1668-1670",

pages = "1668--1671",

booktitle = "EU PVSEC 2023",

note = "38th European Photovoltaic Solar Energy Conference and Exhibition, EU PVSEC 2021, PVSEC 2021 ; Conference date: 06-09-2021 Through 10-09-2021",

}

Iwabuchi, K, Kato, K, Watari, D, Taniguchi, I, Catthoor, F, Shirazi, E & Onoye, T 2021, Demand-Aware Electricity Price Prediction Based on LSTM and Wavelet Transform. in EU PVSEC 2023. 38th European Photovoltaic Solar Energy Conference and Exhibition; 1668-1670, pp. 1668-1671, 38th European Photovoltaic Solar Energy Conference and Exhibition, EU PVSEC 2021, 6/09/21. https://doi.org/10.4229/EUPVSEC20212021-7DV.1.4

TY - GEN

T1 - Demand-Aware Electricity Price Prediction Based on LSTM and Wavelet Transform

AU - Iwabuchi, Koki

AU - Kato, Kenshiro

AU - Watari, Daichi

AU - Taniguchi, Ittetsu

AU - Catthoor, Francky

AU - Shirazi, Elham

AU - Onoye, Takao

N1 - Conference code: 38

PY - 2021

Y1 - 2021

N2 - This paper proposes a novel electricity price prediction method using past electricity demand. The previous method predicts the electricity price using LSTM after wavelet transform of the past electricity price series. Wavelet transform decomposes the series into more smooth and stable series, and accurate prediction is performed by LSTM with these series. The proposed method also uses the electricity demand series, and the demand is also applied wavelet transform for effective LSTM step. Experimental results show the effectiveness of the proposed method, and the accuracy is drastically improved over the previous method.

AB - This paper proposes a novel electricity price prediction method using past electricity demand. The previous method predicts the electricity price using LSTM after wavelet transform of the past electricity price series. Wavelet transform decomposes the series into more smooth and stable series, and accurate prediction is performed by LSTM with these series. The proposed method also uses the electricity demand series, and the demand is also applied wavelet transform for effective LSTM step. Experimental results show the effectiveness of the proposed method, and the accuracy is drastically improved over the previous method.

KW - NLA

UR - https://userarea.eupvsec.org/proceedings/EU-PVSEC-2021/7DV.1.4/

U2 - 10.4229/EUPVSEC20212021-7DV.1.4

DO - 10.4229/EUPVSEC20212021-7DV.1.4

M3 - Conference contribution

SN - 3-936338-78-7

T3 - 38th European Photovoltaic Solar Energy Conference and Exhibition; 1668-1670

SP - 1668

EP - 1671

BT - EU PVSEC 2023

T2 - 38th European Photovoltaic Solar Energy Conference and Exhibition, EU PVSEC 2021

Y2 - 6 September 2021 through 10 September 2021

ER -

Demand-Aware Electricity Price Prediction Based on LSTM and Wavelet Transform

Abstract

Publication series

Conference

Keywords

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