Data Driven Building Retrofit

Buildings consume 30 to 40% of global primary energy use, making them a major source of CO₂ emissions. The International Energy Agency has identified energy efficiency in buildings as one of the five measures to secure long term decarbonisation of the energy sector. This can only be achieved by widescale retrofitting of existing buildings. Although many governments around the world have embarked on efficiency regulations, the current rate of retrofitting buildings is very low. This is due to numerous technological, analytical and economical factors.

Our proposed approach bridges this gap. We call it Data-Driven Retrofit (DDR). We believe DDR will accelerate the retrofit rate and ensure a future with green and sustainable built-environments.

DDR is a novel approach to understand energy performance of buildings and find ways to improve them. DDR begins with deployment of smart sensors. These sense all the relevant building parameters. The data is processed using fundamental principles of building physics combined with machine learning models. This gives a comprehensive understanding of how well or poor your building is performing. If required, it will also suggest cost-optimal building retrofits in an accurate and quick way!