AI Powered Smart System for Forecasting and Optimizing Solar Energy Usage
Kokkula Varshini1, Kasiojjala Meghana2, Madugani Sai Chandana3, Gurrala Harshitha4, E. Shireesha5
1,2,3,4UG Student, Department of Computer Science and Engineering,
Jyothishmathi Institute of Technology and Science, Karimnagar, Telangana, India
5Assistant Professor, Department of Computer Science and Engineering, Jyothishmathi Institute of Technology and Science, Karimnagar, Telangana, India
1varshinikokkula60@gmail.com, 2meghanakasiojjala@gmail.com, 3maduganisaichandana@gmail.com, 4harshithareddy0573@gmail.com
Abstract—Solar power is among the most widely used sources of renewable energy due to its cleanliness, sustainability, and ease of access. However, the generation of electricity from solar energy can fluctuate throughout a day due to changes in sunlight intensity, temperature and weather conditions which creates difficulties for properly utilizing the generated solar energy without accurate assessment/forecasting through adequate monitoring as to how much solar energy will be produced at any given moment. Also, many existing solar systems do not have smart capabilities that provide the user with important information on how to utilize their available solar power or what would be the best way to use it. The proposed project details research into an AI based smart system that forecasts and optimizes how solar energy will produce, through the collection of various environmental data (sunlight intensity, temperature, date, time) to provide a prediction of how much solar energy will be produced by plants and offer valuable information and guidance on household appliances that are ideal for usage when solar energy is abundant. Additionally, the proposed system includes a simple user interface, designed to provide the user with an easy way to track their solar energy production and manage their energy consumption. The proposed smart system aims to increase the efficiency of using solar power and improve the energy use of households by providing users with reliable forecasts of how much solar power will be generated so that users can save on electricity costs and will make better use of renewable energy
