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Using remote sensing technology, we can instantly see where solar panels can be deployed across a city or portfolio, accounting for shading and obstacles, giving full businss cases
About
By using computer vision and machine learning, Absolar’s technology is able to extract building structural information from satellite imagery and LiDAR data, conduct automated feasibility assessments and business cases for buildings to install solar panels, as well as identify existing solar panel installations. This includes shading analysis, install costs, estimated CO2 and cost savings and payback period analysis. All of this is done within a constantly updated digital twin model of the UK, allowing instant analysis of thousands of buildings at once, in detail. When attempting to understand the solar potential of property portfolios, the level of data and differing property factors can quickly become complex and often results in an over-reliance on using solar installers to provide advice, lacking independence. Our remote solar survey tool allows users to simply understand where solar makes sense, and where it doesn't. Additionally, by providing full business cases, the tool allows data-led decision-making in the implementation of decarbonisation and renewable installations.
Key Benefits
Accurate assessment, whether for one or one hundred thousand properties Independent information, free from bias, informed by the science and technology only Significantly reduced costs by instantly understanding where solar does, and crucially where solar does not, make sense, saving manual survey time - for example, surveying a city would take a survey team 4 years, it takes Absolar less than 40 minutes. Know the potential of the portfolio for carbon emissions reductions and fuel bill savings
Applications
Remote Solar Survey of residential and commercial property portfolios throughout the UK Pre-sifting buildings in a portfolio for those capable of installing solar Understanding portfolio solar potential, including CO2 and cost-saving, install cost and production capacity