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

Dounia El Bourakadi (), Hiba Ramadan, Ali Yahyaouy and Jaouad Boumhidi
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Dounia El Bourakadi: Sidi Mohammed Ben Abdellah University
Hiba Ramadan: Sidi Mohammed Ben Abdellah University
Ali Yahyaouy: Sidi Mohammed Ben Abdellah University
Jaouad Boumhidi: Sidi Mohammed Ben Abdellah University

Fuzzy Optimization and Decision Making, 2023, vol. 22, issue 4, No 5, 645-667

Abstract: 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.

Keywords: Energy management system; Electricity price prediction; Bidirectional long-short term memory; Type-2 fuzzy logic control; Decision-making (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10700-022-09406-y

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