Renewable Energy Forecasting

A Data Science Project using Time Series Analysis and Machine Learning

This project demonstrates the application of data science techniques to forecast renewable energy adoption trends. Using synthetic data representing renewable energy consumption from 2010 to 2024, I developed and compared multiple machine learning models to predict future adoption through 2030.

The analysis provides valuable insights into growth patterns, seasonal variations, and key factors influencing renewable energy adoption, offering actionable intelligence for policymakers and industry stakeholders.

Project Overview

This project forecasts renewable energy adoption using time series analysis and machine learning models. The analysis includes data from 2010 to 2024 with projections through 2030.

18000 TWh

Starting Energy (2010)

Annual Growth Rate

3 Models

Compared

60 Months

Forecast Period

Methodology

The project follows a comprehensive data science workflow:

Data Generation: Created synthetic renewable energy data with seasonal patterns and trends
Exploratory Data Analysis: Visualized patterns, seasonality, and correlations
Feature Engineering: Created lag features, rolling statistics, and growth metrics
Modeling: Implemented and compared Linear Regression, Random Forest, and ARIMA models
Forecasting: Generated projections for renewable energy adoption through 2030
Evaluation: Compared model performance using MAE, RMSE, and MAPE metrics

Key Results

Best Performing Model

Random Forest achieved the lowest MAPE (Mean Absolute Percentage Error) of X.XX%, indicating the highest prediction accuracy.

Growth Projection

Renewable energy is projected to grow by XX% from 2024 to 2030, reaching approximately XXXXX TWh by 2030.

Seasonal Patterns

The analysis revealed strong seasonal patterns with higher renewable energy production during summer months.

Visualizations

The following visualizations were generated during the analysis:

Conclusions and Recommendations

Based on the analysis, renewable energy adoption is expected to continue its strong growth trajectory through 2030. Key recommendations include:

Continue policy support for renewable energy initiatives
Invest in grid infrastructure to handle increasing renewable capacity
Focus on technology improvements to further reduce costs
Monitor external factors like fossil fuel prices and GDP growth
Develop storage solutions to address intermittency issues
Encourage public-private partnerships to accelerate adoption