Electric vehicle charging demand forecasting taking into account multiple time scales and dynamic road network information
- 21 November 2025
- journal article
- Published by Elsevier in Electric Power Systems Research
- Vol. 253
Abstract
No abstract availableFunding Information
- Gansu Education Department
- National Natural Science Foundation of China (71763025)
- National Natural Science Foundation of China (61861025)
- National Natural Science Foundation of China (61663021)
- National Natural Science Foundation of China (62363022)
- Natural Science Foundation of Gansu Province (23JRRA886)
This publication has 11 references indexed in Scilit:
- Stochastic modeling of electric vehicle charging and impacts on the gridElectric Power Systems Research, 2025
- Electric vehicle charging load forecasting based on user portrait and real-time traffic flowEnergy Reports, 2025
- Achieving accurate and balanced regional electric vehicle charging load forecasting with a dynamic road network: a case study of Lanzhou CityApplied Intelligence, 2024
- Edible oil wholesale price forecasts via the neural networkEnergy Nexus, 2023
- Electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030Nature Communications, 2023
- Multi-type electric vehicle load prediction based on Monte Carlo simulationEnergy Reports, 2022
- Routing Optimization of Electric Vehicles for Charging With Event-Driven Pricing StrategyIEEE Transactions on Automation Science and Engineering, 2021
- Reinforcement Learning-Based Load Forecasting of Electric Vehicle Charging Station Using Q-Learning TechniqueIEEE Transactions on Industrial Informatics, 2020
- Corn Cash Price ForecastingAmerican Journal of Agricultural Economics, 2020
- Optimization models for electric vehicle service operations: A literature reviewTransportation Research Part B: Methodological, 2019