Building a Recommendation Engine for Sustainable Energy Solutions in Buildings
In this two-month Omdena Challenge, 50 technology changemakers have built a recommender engine to automate and simplify facility energy system upgrades. This will help to reduce energy wastage and carbon dioxide emissions while making buildings more efficient and sustainable.
Existing buildings are the top consumer of energy, the top emitter of carbon dioxide emissions, and it takes 30-50 years for new energy efficiency solutions like LED lighting to fully penetrate our existing buildings One of the biggest challenges with upgrading our existing buildings with energy-efficient technology is identifying cost-effective solutions for individual buildings and asset portfolios.
The current method is both time-consuming and expensive with sales reps to find customers, engineers manually inspecting the building’s existing energy systems, and then designing energy-efficient retrofit solutions, followed by a long sales cycle, low customer conversions, and no long term tracking of the retrofit project’s success.
The main problem this project solved is the manual, time-consuming process to identify, prioritize, and recommend energy-efficient solutions for a particular building and customer. This will result in the building owner spending less on energy, spending less on their building upgrades, and emitting less carbon dioxide. Future iterations of the recommendation engine could also optimize for solutions that improve building occupant health and productivity which has social impacts on renewable energy, microgrid, water conservation, and other environmental solution adoption
The project outcomes
Retrolux has built a platform called Smart Energy scaleOS™ to automate and simplify facility energy system upgrades. You will build a project recommendation engine to the scaleOS™ platform that automatically analyzes individual buildings and asset portfolios to identify, prioritize, and recommend specific energy efficiency and other clean energy solutions that meet the facility owner’s project approval criteria.
The recommendation engine may analyze the building’s physical and energy characteristics, local and regional environment, utility tariffs and incentives, government regulations and incentives, current energy technology costs and performance specifications, and other available information.
The recommendation engine can then identify specific energy-efficient and clean energy solutions for individual buildings, provide means and methods to prioritize the solutions, and ultimately recommend cost-effective solutions for building owner approval based on the owner’s minimum return on investment, carbon reduction, or other goals.
Our goal for this project is a robust overall recommendation engine architecture built specifically for at least one energy efficiency solution like LED lighting, lighting controls, rooftop unit optimization controls, smart motors, super-efficient warehouse freezer doors, etc.