Renewable Energy Optimization

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  Solar weather dataset for optimization

Inspiration

I was inspired by my friend that wrote a research paper. He told me of his novelty idea about renewable energy optimization, then I start working on this project by myself.

What it does

The model will predict the energy delta(Wh) and compare it with the actual energy delta(Wh) based on features from the dataset

How I built it

I followed the OSEMN framework to obtain the data properly, scrub any missing or inconsistent values, explore the pattern then build the model using MLPRegressor in Python. Then I visualize the end result for interpretation and evaluation metrics.

Challenges I ran into

I was confused which model I should use for my solar weather data. I finally decided to use MLPRegressor to predict the energy delta.

Accomplishments that I am proud of

I manage to create the model with decent accuracy by using metrics such as MSE, MAE, R-squared, Explained Variance Score, and RMSE

What I learned

Predicting energy from solar weather features is crucial for the development of renewable energy optimization to build better society based on data-driven decision.

What's next for Renewable Energy Optimization

I found an interesting model that is still at early development, which use the concept of quantum computing, to predict the energy more accurately and faster computing time.

Built With

• python
• sklearn