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Energy consumption forecast in peer to peer energy trading
Faculty
Engineering
Year:
2023
Type of Publication:
ZU Hosted
Pages:
Authors:
Hend Jaballah Hassan Mohamed
Staff Zu Site
Abstract In Staff Site
Journal:
SN Applied Sciences Springer Nature
Volume:
Keywords :
Energy consumption forecast , peer , peer energy
Abstract:
This study predicts future values of energy consumption demand from a novel dataset that includes the energy consumption during COVID-19 lockdown, using up-to-date deep learning algorithms to reduce peer-to-peer energy system losses and congestion. Three learning algorithms, namely Random Forest (RF), Bi-LSTM, and GRU, were used to predict the future values of a building’s energy consumption. The results were compared using the RMSE and MAE evaluation metrics. The results show that predicting the future energy demand with accurate results is achievable, and that Bi-LSTM and GRU perform better, especially when trained as univariate models with only the energy consumption values and no other features included.
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