A Sequence-to-sequence LSTM aproach for forecasting energy consumption and production

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198046" target="_blank" >RIV/00216305:26220/26:0198046 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/EPE67256.2025.11127157" target="_blank" >http://dx.doi.org/10.1109/EPE67256.2025.11127157</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EPE67256.2025.11127157" target="_blank" >10.1109/EPE67256.2025.11127157</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Sequence-to-sequence LSTM aproach for forecasting energy consumption and production

Popis výsledku v původním jazyce

As the energy sector is undergoing a major transformation due to innovation and the growing share of new renewable energy sources, accurate forecasting of electricity production and consumption is crucial for efficient market management and optimization. This paper focuses on the application of advanced deep learning methods to the behaviours of prosumers. The importance of prosumers lies in knowing their own consumption and production in order to effectively utilize their role in the energy sector. To enhance the understanding of consumption and production patterns, a Long Short-Term Memory network with a sequence-to-sequence architecture was implemented to forecast the consumption and production of prosumers. The model was trained and evaluated on real data from residential photovoltaic systems and smart meters over a four-year period. The results show that the model can accurately predict values based on historical data. These models demonstrate the potential of deep learning for improving local energy management and enabling more active participation of prosumers in electricity markets. Future areas of research should focus on how prosumers can interact with the market and what options they have for entering the market to achieve some profit.

Název v anglickém jazyce

A Sequence-to-sequence LSTM aproach for forecasting energy consumption and production

Popis výsledku anglicky

As the energy sector is undergoing a major transformation due to innovation and the growing share of new renewable energy sources, accurate forecasting of electricity production and consumption is crucial for efficient market management and optimization. This paper focuses on the application of advanced deep learning methods to the behaviours of prosumers. The importance of prosumers lies in knowing their own consumption and production in order to effectively utilize their role in the energy sector. To enhance the understanding of consumption and production patterns, a Long Short-Term Memory network with a sequence-to-sequence architecture was implemented to forecast the consumption and production of prosumers. The model was trained and evaluated on real data from residential photovoltaic systems and smart meters over a four-year period. The results show that the model can accurately predict values based on historical data. These models demonstrate the potential of deep learning for improving local energy management and enabling more active participation of prosumers in electricity markets. Future areas of research should focus on how prosumers can interact with the market and what options they have for entering the market to achieve some profit.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proccedings of the 2025 25th International Scientific Conference on Electric Power Engineering (EPE)

ISBN

979-8-3315-8636-2

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

1-4

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Praha

Datum konání akce

27. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001575469500035