Development of ai-based optimum energy resource management system for prosumers with solar rooftops

Abstract

Solar installations are becoming popular around the world and have emerged as a promising solution to address the increased energy needs while reducing carbon emissions. To harness the full potential of solar photovoltaic (PV) systems, efficient resource management systems play a vital role. This research paper proposes an efficient solar PV energy resource management system to optimize performance and increase the profits of the prosumers. Utility providers have introduced several tariff systems for the financial motivation of customers. In the proposed method, the load demand and Solar PV generation are forecasted for the next 48 hours using the Long Short-Term Memory (LSTM) model. Then, the cost function is optimized using the Sequential Least Squares Programming (SLSQP) algorithm, and an energy dispatch schedule is provided for the customer. The results of the study show that the electricity cost is reduced for the prosumer by the proposed method than the conventional rule-based energy management systems.