Industry
Energy
Client
MVP
Integrating Satellite Imagery and Deep Learning for global Solar Power Forecasting 🛰️
About project
A Revolutionary Approach to Identifying Solar Panel Installations and Predicting Solar Energy Production
The integration of renewable energy sources into the power grid is a crucial step towards achieving sustainability and reducing the environmental impact of electricity generation.
This case study of an MVP I've worked on, explores a novel solution that combines satellite imagery, deep learning techniques, and a physical PV model, to identify solar panel installations and accurately predict solar energy production, thereby assisting grid operators in making informed decisions.
Problem
The fluctuating availability of solar energy due to changing weather conditions makes it difficult for grid operators to predict solar power output accurately.
This unpredictability can lead to inefficiencies in energy distribution and challenges in maintaining the stability of the power grid.
Furthermore, the lack of comprehensive data on solar panel installations across different regions hampers the ability of forecasters to make precise energy production predictions.
Solution
I developed a proposed solution that addresses these challenges through a three-step process:
Satellite Image Analysis: By analyzing high-resolution satellite images with deep learning, specifically convolutional neural networks (CNNs), we can detect and map solar panel installations across diverse landscapes. This should creates a current database of solar resources.
Timeline of solar panel installations - by running our detection algorithms over time, we can create a historical representation of solar installations, allowing us to help grid operators and energy suppliers to forecast PV penetration into the future.
Solar Power Forecasting: Utilizing a physical model, we can leverage the mapped solar installations alongside weather forecasts, panel specifications, and geographical data to accurately predict solar energy output.
Forecasting Maps for Grid Operators: We can generate detailed maps forecasting solar power production across regions. These maps could assist grid operators in visualizing energy production fluctuations, facilitating optimized energy distribution, grid stability, and a reduction in dependency on non-renewable sources.
