This project involves forecasting solar power generation using machine learning models. It utilizes historical weather and solar power generation data to predict future energy outputs, aiding in efficient energy management and planning.
Objectives
Analyze Solar Power Data: Understand patterns and trends in solar power generation data.
Develop Predictive Models: Create models to forecast future solar power generation based on historical data.
Optimize Energy Management: Improve the accuracy of solar power predictions to optimize energy resources.
Methodology
1. Data Preprocessing:
Cleaning and preparing solar power and weather data.
Handling missing values and normalizing the data.
2. Exploratory Data Analysis (EDA):
Visualizing data trends and distributions.
Identifying correlations between weather variables and solar power output.
3. Model Development:
Building and training machine learning models, such as Random Forest and Linear Regression, to predict solar power generation.
Evaluating model performance using metrics like RMSE and MAE.
4. Results Visualization:
Comparing predicted vs. actual solar power outputs.
Visualizing model performance through plots and charts.
Methodology
Accurate Predictions: The models provide accurate predictions of future solar power generation, which can be used for better energy resource allocation.
Enhanced Energy Planning: Improved forecasting helps in planning and managing solar power generation more efficiently.
Sustainable Energy Management: Contributes to the optimization of renewable energy resources, promoting sustainability.
