Renewable energy, such as solar and wind, has been increasing in popularity for over a decade. This is especially true in rural, underdeveloped areas, and urban households that desire energy independence. Renewable energy sources, such as solar, provide enhanced environmental benefits while simultaneously minimizing the carbon footprint. One popular technology that can capture solar energy is solar panels. The demand for solar panels has been on the rise due to increases in energy conversion efficiency, long-term financial advantages, and contributions to decreasing fossil fuel usage. However, solar panels need a steady supply of sunlight. This can be challenging in many situations, geographies, and environments. This paper uses multiple machine learning (ML) algorithms that can predict future values of solar radiation based on previously observed values and other environmental features measured without the use of complex equipment with methods that are computationally efficient so that forecasting can be done on consumer premises.
Comparison and interactions between the long-term pursuit of energy independence and climate policies
