Energy Forecasting

Renewable Energy Forecasting System

Problem Statement

Renewable energy production companies are expected to provide the expected production for the next day and if there is a deviation in the actual production from the expected production, they are heavily penalized by the government.

Our Solution

By using historical weather and solar irradiance (GHI) data, state-of-the-art machine learning models are trained and they are used to accurately predict and forecast renewable energy production for the next day.

What makes our solution unique?

The current prediction models used by the renewable energy production companies are not based on artificial intelligence and they can’t predict with high accuracy as most of the factors like weather and solar irradiance tend to vary a lot within a short span of time and a conventional model will not be able to adapt to those changes quickly. Our model which is based on machine learning will be able to learn such anomalies from historical data and provide accurate predictions regardless of the conditions. Having a machine learning model for this use case gives us a huge advantage over the existing players who employ traditional models which have inferior accuracy.