Adaptive energy management strategy for sustainable xEV charging stations in hybrid microgrid architecture | Science and Technology for Energy Transition (STET)

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Table 6

Summary of the existing case studies that analyse EV charging load forecasting.

Case study	Location	Components	Control strategy	Forecasting efficiency (%)	Voltage fluctuation	Key findings	References
EV charging load forecasting in smart grids	Urban area	EV charging stations, solar PV, energy storage	ANFIS	90	±2%	Most accurate forecasting; maximizes renewable energy utilization.	[41]
Impact of ANN on EV charging forecasting	College campus	Charging infrastructure, grid connection	ANN	85	±3%	Accurate load predictions; improved integration of renewables.	[42]
Fuzzy logic approach to EV load prediction	Research institute	EV chargers, grid resources	Fuzzy Logic	78	±4%	Limited ability to forecast loads accurately under variability.	[43]
PID control in EV charging load management	Commercial area	Fast chargers, battery storage	PID	70	±5%	Inefficient load forecasting; less effective in managing fluctuations.	[44]

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