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Impact Factor : 8.14

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Paper Title: EV Charging Demand Forecasting Using Machine Learning
Authors Name: Anurag Pravin Kavitkar , Ritisha Sanjay Shelokar , Rishika Rajiv Deshmukh , Minakshi Shankar Gawande , Sakshi Rameshwar Shende
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IJRTI_210568
Published Paper Id: IJRTI2603126
Published In: Volume 11 Issue 3, March-2026
DOI:
Abstract: Fast growth in Electric Vehicle ownership reshaped how people move, pushing utilities to adapt with cleaner power sources and smarter grids. Still, more cars charging on batteries brings tough problems - like figuring out true energy needs before building stations, keeping electricity supplies steady, managing waste in the system. Predicting exactly when and where charging happens remains hard, affecting where chargers go, how much users pay moment by moment, balancing power flow across the network. This study introduces an all-in-one setup able to forecast hourly electric vehicle charging needs - how many sessions, plus total energy used per hour. Using smart data methods, like Random Forest tuning, it breaks down key drivers shaping that demand. Data drawn here mimics actual charging habits but was created artificially. Added details come from schedules (time of day, day of month, season), environmental factors (heat, moisture levels), road traffic pressure, plus flags for public holidays. Trained on real-world data, the new system predicts energy needs during peak hours with strong accuracy. Instead of guessing, it learns from patterns in time and weather through metrics like RMSE and MAE. Built around clarity, the tool uses a user-friendly Streamlit interface where people can see actual demands unfold. By adjusting settings within the dashboard, users test different futures - seeing what might happen under various conditions. This way, choices about charging infrastructure become grounded not just in theory but in live interactions with the system. This setup adjusts easily when fed actual data from current EV systems. Instead of rigid structures, it grows by learning from real-world inputs. Down the road, tools like deep learning could be woven in, possibly even feedback systems guided by machine-based trial-and-error. Live sensor readings from smart chargers might flow directly into its operations. The bigger picture? A network that adapts, learns, responds - not just present but actively part of shaping cleaner energy transport across regions. The proposed framework is evaluated using standard error metrics, demonstrating reliable forecasting performance under peak demand conditions.
Keywords: Electric Vehicles (EVs), Charging Demand Forecasting, Machine Learning, Smart Grids, Random Forest, Energy Management, Data Analytics.
Cite Article: "EV Charging Demand Forecasting Using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b212-b226, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603126.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2603126
Registration ID:210568
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier):
Page No: b212-b226
Country: Paratwada, Maharashtra, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603126
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603126
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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