Advanced Analytics

Data always speaks and is always predictive.

Seigel (2013) notes that this “data effect” is a leap of faith for organizations to take prior to engaging in predictive analytics. They often don’t know that they will find prior to exercising their data muscles. Fortunately, the developers of Res-Intel have validated real-world billing and real estate data and the results show that massive benefits can accrue from systematically combining re-purposed real estate data with building energy regression modeling.

Res-Intel goes beyond predictive analytics by providing customized, specific recommendation that prescribe cost-effective DSM for each single family residence!


Regression Modeling

Our understanding of building energy consumption can be enhanced through numerical modeling efforts. Res-Intel utilizes proven building energy regression modeling techniques that have a successful track record in identifying energy efficiency opportunities.Most building energy modeling is performed with DOE-2 simulation models that typically overestimate the energy efficiency savings available from retrofits, often by 100% or more. These energy models require an incredible amount of detailed building data. LBNL’s Home Energy Scoring Tool, a standardized energy rating for home buyers and sellers, requires users to input attic and foundation insulation levels, window areas, window types, window glazings, frames, window fill, U-Factor and a solar heat gain coefficient. Acquiring this information on widespread basis to rate all residential single family buildings’ energy ratings is impossible. Res-Intel’s regression modeling approach uses parsimonious building data to predict energy consumption and savings opportunities.

Variables Accounting for Energy Use Predictions

Variables Accounting for Energy Use Predictions
Regression Model Performance: Climate and building information explains almost 30% of the variation in building electricity use, while more detailed information contributes only another 6%.