Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data

How to estimate the potential electricity generation of solar panels any building of a country? In this blogpost, we present nam.R’s methodology to solve this problem.

Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data

How could we automatically estimate the amount of electricity that solar panels could generate on any building of a country, instantly? It would require to be able to massively compute the roofs topology and their weather condition as well as other key components such as:

Although this information may be available at the scale of a city, it rarely exists at the scale of a country.

At nam.R, we used machine learning models trained on various data to predict the missing information and make this large scale estimation possible. We process open data (1) to train machine learning models (2) to recognize rooftops on aerial images (3) and predict their inclination (4), which enable us to estimate the maximum number of installable modules and their power generation (5). 

Illustration of the five steps constituting our methodology

Illustration of the five steps constituting our methodology.

 

This article presents our methodology applied on the French territory. We also submitted these results to the Applied Machine Learning Days conference #AMLDays2020 at EPFL (Lausanne, Switzerland).

 

Why is Predicting the Solar Potential of Rooftops Important?

Before we go straight into the solution, we need to step back and look at the context. In our future world of 10 billion people, meeting the energy demand while limiting global warming is a major challenge. Yet, fossil fuels are still our main source of electricity and heat generation, accounting for 42% of the greenhouse gas emissions worldwide in 2016. To lower these numbers, energy efficiency and development of renewable energy are presented as the two main approaches.

Solar panels are one of the fastest-growing renewable energies, despite the presence of non-recyclable materials and a low average efficiency of 15% compared to 50% to 90% for hydropower or wind power. But their operation and maintenance costs are low, and modules can be installed as well on the ground as on top of a roof: useful for cities, where space is limited resource. Estimating the production of solar panels on a large scale is therefore a key challenge to accelerate this transition.

 

Combining open data and AI

At nam.R, we are building a digital representation of the world to perform ecological, economical and digital transformations. As a data producer, we aggregate, clean and re-organize data from various sources that can help to solve real world problems at a large scale. To solve this problem, we decided to use computer vision to identify the roofs and structured data to predict or compute their characteristics.

 

Discover the full article here.

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