Optimization For Maximum ROI
Energy Optima©
Empowered Energy Solutions is able to offer optimized energy efficiency upgrade solutions through the use of our patent-pending, proprietary Energy Optima computational intelligence algorithm.

Our software program combines the information from the diagnostic exam with our proprietary databases of possible upgrades and current incentives to reach an optimized solution delivering maximized return on investment.

The Energy Optima program then examines the range of possible upgrades, discarding possibilities that deliver less than optimal solutions to reach the upgrade scenario that delivers the highest return.

THE OPTIMIZATION PROCESS
Our key advantage is the proprietary optimization process.  Because of diminishing returns one cannot assume that all efficiency improvements are cost effective.  Currently the most sophisticated analysis available consists of choosing a set of improvements by rules of thumb or the judgment of an expert and running an energy analysis program to see if the changes improve the building energy efficiency enough.  At best the program might be run a few times to explore alternatives. This is very far from being a true optimization.

Consider a building in which an energy audit has identified 20 possible improvements which may or may not be cost effective depending on how they interact with each other. Assume that for each improvement there are three alternatives: do nothing or chose one of two grades of improvement (windows of 2 qualities, insulation of 2 thicknesses etc). To find the best combination of improvements by brute force calculation would require running the energy analysis program 3 to the 20th power or 3.4 million times.  At three seconds per run this would take 330 years.  Further this is a problem whose difficulty increases exponentially as the complexity increases.  If there were 21 possible improvements there would be more than 10 billion runs needed for a brute force solution.

EES has identified a method of systematically searching the range of possible combinations of improvements to find the combination with the best return on investment. We have filed a patent application on this method and have partnered with Natural Selection Inc. to implement the required software. Natural Selection is a computer science consulting firm with wide experience in implementing evolutionary computation.