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

Nikolaos Tsalikidis, Paraskevas Koukaras (), Dimosthenis Ioannidis and Dimitrios Tzovaras
Additional contact information
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
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

Energies, 2025, vol. 18, issue 6, 1-24

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.

Keywords: electric vehicle charging; recommender system; dynamic load management; smart grid; forecasting; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/6/1528/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/6/1528/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:6:p:1528-:d:1616006

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().