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Towards an energy management system based on a multi-agent architecture and LSTM networks

Bouziane, Seif Eddine ; Khadir, Mohamed Tarek

Journal of experimental & theoretical artificial intelligence, 2024-05, Vol.36 (4), p.469-487 [Periódico revisado por pares]

Abingdon: Taylor & Francis

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Título:
Towards an energy management system based on a multi-agent architecture and LSTM networks
Autor: Bouziane, Seif Eddine ; Khadir, Mohamed Tarek
Assuntos: agent-based architecture ; Alternative energy sources ; Carbon dioxide ; Clean energy ; Computer architecture ; Deep learning ; Emissions ; Energy management ; Energy resources ; Forecasting ; Greenhouse gases ; Hybrid systems ; LSTM ; Mathematical models ; Multiagent systems ; Neural networks ; renewable energy ; Renewable energy sources ; Renewable resources ; short-term forecasting
É parte de: Journal of experimental & theoretical artificial intelligence, 2024-05, Vol.36 (4), p.469-487
Descrição: Energy generation and pollutant emissions are two faces of the same coin, as the current energy sources i.e., fossil energy, are still considered to be major sources of greenhouse gases (GHG). Therefore, shifting to cleaner energy sources imposes itself as an inevitable solution to reduce this environmental cost. In this paper, a hybrid system based on the multi-agent approach and Long Short-Term Memory (LSTM) neural networks to forecast the energy production and its carbon dioxide (CO 2 ) emissions and simulate the potential emission reduction in case of switching to renewable energy sources is presented. The proposed system's architecture consists of combining LSTM models with the agent-based technology, where multiple LSTM forecasting models were trained to forecast the production of each type of the studied energies and then estimate the equivalent emitted CO 2 and calculate the influence of the renewable energy inputs on the carbon emissions and the fossil fuels consumption. The simulation process consists of two phases: firstly, each forecasting agent uses a specific LSTM model to forecast short-term energy production. Secondly, these agents send the forecasted values to the coordination agent who is responsible for calculating the total CO 2 emissions and the benefits of the renewable energy inputs.
Editor: Abingdon: Taylor & Francis
Idioma: Inglês

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Towards an energy management system based on a multi-agent architecture and LSTM networks

Bouziane, Seif Eddine ; Khadir, Mohamed Tarek

Abingdon: Taylor & Francis

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