LAUSR

Abstract Renewable energy sources are economically competitive choices for indigenous environmental solutions to produce conventional power, which offers high wind and solar resources to optimize resource management. Further, this resolution improves power storage and standby generation, leading to loss of revenue. Alternatively, better forecasts and demand for wind and solar energy allow advanced monitoring and optimization systems to replace costly equipment with lenient models. This research effort underway is consistent with the development of data-derived modeling through the use of machine learning technology. Furthermore, Hybrid plants will enhance the smart and sustainability of renewable energy systems economically and environmentally to meet energy demand. In this paper, the Heuristic Intelligent Neural Decision Support System (HINDSS) can be used to improve local energy production and forecast energy demand through optimized algorithmic approach on smart renewable energy systems. The experimental results show that the implementation of the smart system forecast on the renewable wind and solar energy production helps to reduce the demand for electricity at adequate time and increase the accuracy for an efficient replacement for alternative renewable energy via storage capacity.