Essess combines data captured using our thermal imaging system with utility data, weather data, property records and demographic information to deliver a highly personalized energy efficiency analysis for each building envelope. Combining multiple sources of information enables our analytical engine to identify the optimal structural improvements for each utility customer. The below diagram illustrates the key information inputs to the analytical engine:
Account number and address – enables the system to combine utility data and public domain data sources to relate each thermal image to the specific utility customer.
Weather data – hourly temperature data from the National Climate Data Center’s Quality Controlled Local Climatological Databank (QCLCD) enables access to thousands of weather stations to determine the outdoor temperatures for each utility customer’s location. This information is used to analyze the thermal images by disaggregating either monthly or hourly billing data into heating and cooling components and to calculate savings from efficiency and retrofit measures.
Thermal imaging – enables the customer to visualize the energy leaking from their home and it allows it Essess to estimate savings potential, compare users to their average and efficient neighbors, and identify specific features of the home for targeted improvements.
Property records – enables more accurate recommendations of efficiency and retrofit measures by utilizing the public domain data on file at local county tax assessment offices. This data also increases the engine’s ability to identify similar homes for comparison, better disaggregate home energy use, estimate savings from efficiency and retrofit opportunities, and better interpret thermal images.
Demographic data – identifies specific household characteristics that can maximize conversion into efficiency and retrofit measures by enhancing the effectiveness of marketing campaigns.
Billing data – hourly or monthly meter data that improves the accuracy of energy end use disaggregation and savings estimates from building envelope improvements.
Data normalization – converts all of the diverse streams of data into common units of time and energy to enable simple, easy to understand comparisons.
Analysis engine – processes all of the aforementioned data, including home characteristics, weather, thermal images and bills to disaggregate energy consumption into major end uses (heating, cooling, water heating, and appliances/lighting), estimate the cost of energy leakage based on thermal images, calculate optimal normative comparisons, recommend the best local contractors, and target direct, email and web marketing campaigns using demographics and psychographic information.
Recommendation engine – provides personalized and actionable recommendations on building envelope improvements, product installations and behavioral changes that can reduce energy leakage and save utility customers money. The recommendation engine uses the outputs of the analysis engine to extract from the universe of all available efficiency measures the ones that are most relevant based on building characteristics, savings potential, and payback period. The engine accounts for estimated utility customer income where possible as a proxy for willingness to pay, which assists in the analysis of whether to recommend DIY or contractor measures.