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Smart Charging Recommendation Framework for Electric Vehicles: A Machine-Learning-Based Approach for Residential Buildings

Author

Listed:
  • Nikolaos Tsalikidis

    (Information Technologies Institute, Centre for Research & Technology, 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece)

  • Paraskevas Koukaras

    (Information Technologies Institute, Centre for Research & Technology, 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece
    School of Science and Technology, International Hellenic University, 14th km Thessaloniki-Moudania, 57001 Thessaloniki, Greece)

  • Dimosthenis Ioannidis

    (Information Technologies Institute, Centre for Research & Technology, 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece)

  • Dimitrios Tzovaras

    (Information Technologies Institute, Centre for Research & Technology, 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece)

Abstract

The transition to a decarbonized energy sector, driven by the integration of Renewable Energy Sources (RESs), smart building technology, and the rise of Electric Vehicles (EVs), has highlighted the need for optimized energy system planning. Increasing EV adoption creates additional challenges for charging infrastructure and grid demand, while proactive and informed decisions by residential EV users can help mitigate such challenges. Our work develops a smart residential charging framework that assists residents in making informed decisions about optimal EV charging. The framework integrates a machine-learning-based forecasting engine that consists of two components: a stacking and voting meta-ensemble regressor for predicting EV charging load and a bidirectional LSTM for forecasting national net energy exchange using real-world data from local road traffic, residential charging sessions, and grid net energy exchange flow. The combined forecasting outputs are passed through a data-driven weighting mechanism to generate probabilistic recommendations that identify optimal charging periods, aiming to alleviate grid stress and ensure efficient operation of local charging infrastructure. The framework’s modular design ensures adaptability to local charging infrastructure within or nearby building complexes, making it a versatile tool for enhancing energy efficiency in residential settings.

Suggested Citation

  • Nikolaos Tsalikidis & Paraskevas Koukaras & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2025. "Smart Charging Recommendation Framework for Electric Vehicles: A Machine-Learning-Based Approach for Residential Buildings," Energies, MDPI, vol. 18(6), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1528-:d:1616006
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    References listed on IDEAS

    as
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