**Our Vision**

PassivSystems has developed a new way of remotely characterising the thermal performance of domestic properties that addresses key challenges that have arisen as a consequence of COVID-19 and provide useful tools to help mitigate climate change. Through this project, we are proposing to build on our advanced monitoring and modelling capabilities to deliver an innovative service targeted at private homeowners and social landlords. The objectives of our proposed service are to:

1. Reduce the need for in-home surveys and potential COVID-19 infection risks from assessors visiting multiple properties
2. Provide a unique digital thermal performance calculation to model the impact of installing low-carbon measures
3. Improve the targeting of energy-efficiency measures to reduce fuel poverty and support economic recovery
4. Deliver ongoing environmental quality monitoring in homes leading to better health outcomes, particularly for respiratory conditions associated with COVID-19

**The Insight Service**

The Insight Service will use wireless in-home sensors to provide energy modelling and environmental monitoring. The sensors we are proposing to use are extremely easy to install and would be posted to residents. They use an ultra-low-power, narrowband communication protocol to capture temperature, humidity and CO2 readings from a building. Data from the sensors will be collected via our platform and combined with smart-meter data, external temperatures from weather stations and physical characteristics (from a recent EPC or from an online form completed by the resident).

The core innovation is the use of PassivSystems machine-learning to provide sophisticated modelling of a building's thermal characteristics. This modelling will help landlords and homeowners assess the in-situ energy performance characteristics of individual homes. Additional data from humidity and CO2 sensors will provide insight on a property's environmental health enabling ventilation, damp and mould risks to be assessed and to highlight risks to health.

We will develop a new dashboard-interface to present our data. Landlords will be able to make logical groups (for example by age, heating, interventions), track aggregate performance metrics across a portfolio and deploy big-data analytics to identify real-time performance issues. We will also develop new reporting capabilities to support comparison analysis, trends and avoid false alerts that create unnecessary callouts, thereby further reducing the need for unnecessary social contact risks.

In the UK, domestic energy use is responsible for more than a quarter of carbon emissions. But assessing the most appropriate route to reduce these emissions to help mitigate climate change is a significant challenge. The ambition of this project is to demonstrate that easy to install, remote sensors can deliver quantifiable insights on both energy and environmental performance directly to landlords and homeowners. Our machine learning technology will also provide accurate performance monitoring and evaluation of low-carbon heating systems.

We believe that by using remote sensors that actively reduce the need for multiple potentially high-risk home energy survey visits, the Insight Service will not only be able to help improve the environmental quality of homes through ongoing monitoring but will also help to unlock the investment needed to deliver large scale low carbon retrofit using quantifiable thermal performance modelling.