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Optimization of Sizing of Battery Energy Storage System for Residential Households by Load Forecasting with Artificial Intelligence (AI): Case of EV Charging Installation

Author

Listed:
  • Nopphamat Promasa

    (Department of Electrical Engineering, Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi (RUS), Nonthaburi 11000, Thailand)

  • Ekawit Songkoh

    (Department of Industrial Engineering, Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi (RUS), Phra Nakhon Si Ayutthaya 13000, Thailand)

  • Siamrat Phonkaphon

    (Department of Electrical Engineering, Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi (RUS), Nonthaburi 11000, Thailand)

  • Karun Sirichunchuen

    (Department of Electrical Engineering, Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi (RUS), Nonthaburi 11000, Thailand)

  • Chaliew Ketkaew

    (Department of Electrical Engineering, Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi (RUS), Nonthaburi 11000, Thailand)

  • Pramuk Unahalekhaka

    (Department of Electrical Engineering, Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi (RUS), Nonthaburi 11000, Thailand)

Abstract

This paper presents the optimization sizing of a battery energy storage system for residential use from load forecasting using AI. The solar rooftop panel installation and charging systems for electric vehicles are connected to the low-voltage electrical system of the Metropolitan Electricity Authority (MEA). The daily electricity demand for future load forecasting used the long short-term memory (LSTM) technique in order to analyze the appropriate size of the battery energy storage system (BESS) for residences. The solar rooftop installation capacity is 5.5 kWp, which produces an average of 28.78 kWh/day. The minimum actual daily load in a month is 67.04 kWh, comprising the base load and the load from charging electric vehicles, which can determine the size of the battery energy storage system as 21.03 kWh. For this research, load forecasting will be presented to find the appropriate size of BESS by considering the minimum daily load over the month, which is equal to 102.67 kWh, which can determine the size of the BESS to be 17.84 kWh. When comparing the size of BESS from actual load values with the load from the forecast, it can significantly reduce the size and cost of BESS.

Suggested Citation

  • Nopphamat Promasa & Ekawit Songkoh & Siamrat Phonkaphon & Karun Sirichunchuen & Chaliew Ketkaew & Pramuk Unahalekhaka, 2025. "Optimization of Sizing of Battery Energy Storage System for Residential Households by Load Forecasting with Artificial Intelligence (AI): Case of EV Charging Installation," Energies, MDPI, vol. 18(5), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1245-:d:1604708
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    References listed on IDEAS

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