The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles

Abstract

Electric vehicles are fully ecological means of transport only when the electricity required to charge them comes from Renewable Energy Sources (RES). When building a photovoltaic carport, the complex of its functions must consider the power consumption necessary to charge an electric vehicle. The performance of the photovoltaic system depends on the season and on the intensity of the sunlight, which in turn depends on the geographical conditions and the current weather. This means that even a large photovoltaic system is not always able to generate the amount of energy required to charge an electric vehicle. The problem discussed in the article is maximization of the share of renewable energy in the process of charging of electric vehicle batteries. Deep recurrent neural networks (RNN) trained on the past data collected by performance monitoring system can be applied to predict the future performance of the photovoltaic system. The accuracy of the presented forecast is sufficient to manage the process of the distribution of energy produced from renewable energy sources. The purpose of the numerical calculations is to maximize the use of the energy produced by the photovoltaic system for charging electric cars.

Keywords

• photovoltaic system
• electric vehicle
• deep recurrent neural networks
• machine learning
• numerical calculation
• applications