Short-Term Heat Demand Prediction Using Deep Learning for Decentralized Power-To-Heat Solutions

Pavani Ponnaganti, Jayakrishnan R. Pillai, Birgitte Bak-Jensen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

1 Citationer (Scopus)

Abstract

Coupling power and heat sectors will contribute to the integration of renewable energy but also promotes decarbonization of heat sector. Better heat demand forecasting is required for reducing the costs and increasing the energy efficiency. The traditional forecasting methods faces difficulties due to the irregularities in data, computational costs and generalizing the output. Application of machine learning based data-driven techniques provides better accuracy in handling data with nonlinear characteristics. This paper proposes the application of Long Short Term Memory (LSTM) based model to forecast the heat energy consumption of a pool of residential consumers considering the real data set. The prediction accuracy is improved by using wavelet transform for data preprocessing and the proposed model gives a better performance.

OriginalsprogEngelsk
TitelProceedings - 2023 IEEE PES GTD International Conference and Exposition, GTD 2023
RedaktørerMehmet Tahir Sandikkaya, Omer Usta
Antal sider5
ForlagIEEE
Publikationsdato2023
Sider11-15
Artikelnummer10261485
ISBN (Elektronisk)9781728170251
DOI
StatusUdgivet - 2023
Begivenhed2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023 - Istanbul, Tyrkiet
Varighed: 22 maj 202325 maj 2023

Konference

Konference2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023
Land/OmrådeTyrkiet
ByIstanbul
Periode22/05/202325/05/2023
SponsorEnerjiSA, IEEE PES, Omicron Bahrain, SEL

Bibliografisk note

Publisher Copyright:
© 2023 IEEE.

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