Enhanced energy planning in Jammu and Kashmir through AI-driven solar power forecasting

The growing population and higher standards of living are driving an increased demand for sustainable energy solutions, with the combination of solar power with the grid providing an opportunity for green power generation. In the Jammu and Kashmir region, characterized by diverse weather patterns and geographical complexities, precise solar power forecasting becomes imperative for better energy planning. This chapter proposes a comprehensive approach leveraging artificial intelligence (AI) for solar power forecasting in this region. Using machine learning algorithms with meteorological information, geographical features and historical solar power generation statistics, our approach aims to improve the accuracy and consistency of solar power forecasts. We explore various AI models and ensemble methods to depict the complex relationships between weather conditions and solar power generation. In this chapter, we employ three widely used regression techniques like Gaussian process, tree-based methods, and linear regression. We explore the experimental methods and validate the effectiveness of our approach in improving long-term solar power forecasts. This consequently enables a more effective utilization of solar energy resources for sustainable energy future in Jammu and Kashmir.