The main aim of this project is to design a machine learning model that can accurately predict the amount of renewable energy such as solar or wind energy that will be generated at a given time. Traditional forecasting methods often rely on basic statistical tools or historical data trends, which may not always yield reliable predictions. This project enhances prediction accuracy by incorporating current weather data and leveraging a sophisticated statistical method, multiple linear regression. The objective is to provide actionable insights that help optimize energy resource management, reduce dependency on non-renewable energy, and maintain a stable and efficient power supply system. By completing this project, students will gain hands-on experience in applying data science and machine learning to solve real-world energy challenges.
To complete this project, students will undertake a variety of tasks spread over a twelve-week timeline. The project begins with an introduction to the basics of machine learning and multiple linear regression. Students will then proceed to explore and import essential Python libraries required for building regression models. The initial weeks focus on setting up the working environment using tools such as Anaconda Navigator or Google Colab.
Subsequent tasks include designing a model framework, sourcing or creating a dataset, training the model using diverse inputs to predict energy production, and testing its accuracy using new datasets. Students will improve the model's prediction capabilities through iterative refinement and will present their final results after rigorous testing and validation. The final stages of the project involve documentation and a team presentation. Throughout the process, students must follow ethical coding practices, maintain accurate records, and avoid plagiarism.
