Machine Learning Based Optimal Solar Power Forecasting Methodology

In current scenario of energy grids, the penetration of solar power is increasing. This increased stake of solar power is posing a threat due to its stochastic behavior. To cope up this issue either extra battery can be installed or forecasting can be performed. Extra battery will increase the capital cost of the system, so forecasting provide an efficient alternative for the same. Machine learning has emerged as a promising approach for accurately forecasting solar power generation. This work presents a state vector machine based solar power determining framework that uses verifiable solar power information, climate information, and other pertinent factors to foresee future solar power age. The framework is prepared on a huge dataset utilizing different AI calculations and assessed utilizing different high layered execution measurements. The outcomes show that the proposed framework can accomplish high precision and beat conventional guaging strategies. The created framework can possibly work on the effectiveness of solar power frameworks and empower better dynamic in the energy area.