Using Machine Learning Techniques To Plan A Fully Renewable Energy Systems By The End of 2050: Empirical Evidence From Jerusalem District Electricity Company

Abstract

Machine Learning (ML) approach facilitates solving many current issues, such as marketing, telecommunication, health care, sale, and energy issues. ML solves the issue of burning fossil fuels that caused environmental pollution by replacing conventional resources with renewable resources. This study aims to advance the knowledge of transitioning the Palestinian energy system to 100% renewable energy towards 2050 and considering the mostly expected load demand in the period of 2020–2050. The load performance and the share of renewable energy sources for the existed pattern of a given data for the status in Palestine are forecasted by using the Artificial Neural Network method (RNN). Then by using the linear programming method, the needed share of renewables into the grid is designed. The results of analyses show that 100% renewable energy is thinkable to be achieved.