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A robust energy management approach in two-steps ahead using deep learning BiLSTM prediction model and type-2 fuzzy decision-making controller

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Abstract

The price prediction is valuable in energy management system (EMS) because it allows making informed decisions and solving the problem of the uncertainty related to the future ignorance based only on the past knowledge. To this goal, we present in this paper a two-steps EMS in order to control the different operations of a micro-grid (MG). In the first step, we exploit the advantages of the Bidirectional Long-Short Term Memory (BiLSTM) deep learning model to predict the next daily electricity price based on time series. In the second step, we use a type-2 fuzzy logic controller to decide which energy source will exploit the excess energy produced or meet the MG need. Real data is used in this paper to test the effectiveness of the proposed EMS whose superiority is proved through the test period. The BiLSTM forecasting model better performs compared to other related algorithms designed to the electricity price prediction. In addition, the proposed decision-making process can reduce the total MG cost and protect the batteries against the deep discharge and maximum charge in order to prolong their lifespan. We expect that this work can contribute to meet the real-world needs in the management of the electrical system.

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Authors and Affiliations

  1. LISAC Laboratory, Department of Computer Sciences, Faculty of Sciences Dhar Mehraz, Sidi Mohammed Ben Abdellah University, 30003, Fez, Morocco

    Dounia El Bourakadi, Hiba Ramadan, Ali Yahyaouy & Jaouad Boumhidi

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  1. Dounia El Bourakadi
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  2. Hiba Ramadan
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  3. Ali Yahyaouy
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  4. Jaouad Boumhidi
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Correspondence to Dounia El Bourakadi.

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El Bourakadi, D., Ramadan, H., Yahyaouy, A. et al. A robust energy management approach in two-steps ahead using deep learning BiLSTM prediction model and type-2 fuzzy decision-making controller. Fuzzy Optim Decis Making 22, 645–667 (2023). https://doi.org/10.1007/s10700-022-09406-y

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