Artificial neural network-based models for short term forecasting of solar PV power output and battery state of charge of solar electric vehicle charging station

The main objective of this study is to develop ANN-based predictive models for short-term forecasting
of solar PV power output and battery state of charge. The 3Ds energy model that integrates
Decarbonization, Digitalization, and Decentralization of the energy system to facilitate the
shift towards sustainable energy sources is used for this electric vehicle project. The experimental
set up includes solar PV panels, a solar inverter, a battery inverter, and a battery bank. Additionally,
smart meters were installed to collect real-time performance data from the solar PV-powered
electric vehicle (EV) charging station. The weather and real-time system performance data were
used to develop short term forecasting models based on Artificial Neural Networks (ANN) and the
Levenberg-Marquardt method, to predict the performance of the system (power output and state
of the charge) ahead. The R values for the prediction models of solar photovoltaic (PV) specific
power and battery state of charge fall within the range of 0.9957–0.9969 and 0.9990 to 0.9996,
respectively. Furthermore, the mean squared errors (MSEs) for the artificial neural network
(ANN) models pertaining to solar photovoltaic (PV) power output and battery state of charge exhibit
a variety of values. Specifically, the MSEs for solar PV power output range from 1.242 x 10^-
4 to 1.579 x 10^-4, while the MSEs for battery status of charge range from 0.1889 to 0.3402. Artificial
neural network (ANN) models possess considerable promise for practical implementations
as they simplify intricate connections among inputs, parameters, and outputs in real-world scenarios.
The level of accuracy and short-term predictive models of the solar PV powered EV charging
station are very important for achieving a balance between the supply of solar photovoltaic
(PV) system and the demand for electric vehicles (EVs). Predictive models will help build complex
control mechanisms for controlling and optimizing solar PV-powered charging stations, supervising
their operation and maintenance, and simplifying renewable energy pre-purchase. Future
works will include the development of an energy management system to improve the efficiency
of the solar stations charging the EVs, the establishment of blockchain networks for both
the solar PV system and battery bank, and the integration of cybersecurity protocols for the
charging station.