Energy consumption forecast in peer to peer energy trading

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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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APA

Hassan, H. G., Shahin, A. A., & Ziedan, I. E. (2023). Energy consumption forecast in peer to peer energy trading. SN Applied Sciences, 5(8). https://doi.org/10.1007/s42452-023-05424-6

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