Predicting Solar Energy Production

Project Overview

A regression forecasting project designed to predict annual solar energy output. Helps developers select optimal geolocations, select equipment types, and forecast future power yields.

Development Process

Processed physical parameters, weather datasets, panel technology classes, and sun exposure ratios. Trained regression estimators including XGBoost, Ridge, and Random Forest Regressors, tuning hyperparameters using GridSearch to minimize Root Mean Squared Error (RMSE).

Key Features

• Regression model forecasting annual solar energy production with high accuracy
• Exploratory analysis of weather patterns, tracking solar irradiance parameters
• Hyperparameter optimization using GridSearchCV to tune regression algorithms
• Geographic correlation mapping of energy yield based on latitudinal datasets
• Automated pipeline ingestion of solar telemetry logs

Challenges & Solutions

Dealing with highly correlated meteorological variables (like temperature, humidity, and direct solar irradiance) causing multicollinearity. Resolved this by using Ridge Regression regularization and tree-based modeling (XGBoost) which are robust to multicollinearity.

Technologies Used