Contents I. Introduction
Renewable energy (RE) provides a promising alternative to conventional energy, but its development necessitates environmental tradeoffs. As a result, to overcome the detrimental environmental impacts and depleting fossil fuels reserves, many countries have been forced to consider RE sources to meet rising energy demand [1]. These are more environmental friendly and will need to be increased as a result of the impending oil crisis and the escalation of rural electrification efforts. These financial limits, however, can be overcome if proper technology is used to develop systems that are focused on providing effective load forecasting and dispatching strategies. As an alternative to oil-produced energy, the combined use of RE sources can be investigated. For the safety of RE use, accurate forecasting of solar energy estimation is critical. The goal of this research is to examine solar energy sources in order to determine their cost-effectiveness and scalability. For the previous few decades, solar energy has progressed at a faster rate than other renewable energy sources. The countries that are researching solar energy, on the other hand, are not always those who have the most access to this resource. Although Iceland and Norway are not among the top five countries with the most installed and actual solar power capacity, they now generate all of their electricity with RE. Seeing their success, many other countries pledged to replace existing power infrastructure and reach 100% RE in the future [1], [8]. M. Rizwan et al. proposed a neural network based scheme for predicting the global solar energy. Any country located in the earth's equatorial solar belt, receives a lot of radiant energy from the sun. India being one of them. Themean relative errorfor global solar energy was estimated to be 4%, but it was increased to 6% when fuzzy logic was utilized [2]. For the United Kingdom, S. Abu-Bakar et al. implicated a renewable heat incentive plan in solar thermal installations. It was discovered that, depending on the area, the plan could create a good profit [3]. Solar thermal power plants were examined by V. Siva Reddy et al. as a way to meet the rising demand for conventional energy. [4] Case studies of 50 Megawattin Indian climatic conditions using solar thermal power plants were emphasized at Jodhpur and Delhi. R.K. Akikur et al. investigated solar energy as a standalone and hybrid power generation system for electrification of off-grid locations. In a hybrid system, it was discovered that the weakness of one source might be compensated for by the other source [5]. R. R. Hernandez et al. examined the characteristics and development strategies of such systems, noting that they have low environmental consequences when compared to other energy systems, including renewables [6]. S. C.S. Costa et al. examined solar electric device soiling to update the compendium, which covered the period from 2012 to 2015 [7]. K. Bradbury et. al. (2014) explored the economic viability of energy storage systems based on price arbitrage potential in real time. They showed that the work could be more profitable with minimal cost reductions by reducing energy capacity costs [8]. G. Anandarajahet. al. (2014) analyzed the role of renewable to meet India's possible 2050 climate change mitigation targets using a multi-region global energy system model. Analysis proved that the renewable energy definitely plays an important role to boost the economy, especially in the power sector [9]. Solar energy as a cost-competitive and inherently dependable energy source for utility-scale electricity production as well as electricity, heat, and light for all types of buildings around the world [11]–[16].
