Exploring Machine Learning Models for Solar Energy Output Forecasting

Engineering, science, health, and other fields have all used machine learning algorithms. The idea of machine learning is used in this study to forecast solar energy output. Predicting solar energy, a well-known renewable source with a number of advantages, can help with energy consumption planning. Inconsistent weather makes it difficult for grid operators to manage solar energy output, which makes it harder to satisfy customer demand. Utilizing various algorithms including Lasso, Ridge, Linear and Support Vector Regression (SVR) algorithms, our suggested strategy entails developing prediction models. These algorithms produce forecasts based on past weather information such as temperature, dew point, wind, cloud cover, and visibility. SVR algorithm outperformed the other algorithms, according to the Jupyter Notebook examination.