Lack of data, that's the problem. When considering renewable energy investments it's hard to evaluate new technologies and investment feasibility. We wanted to make it simple to compare different renewable energy technologies and see the difference it would make.
What it does
Our Predix application analyses building energy consumption and builds consumption profile for the building. Based on that our service suggests few suitable technologies and evaluates energy saving potential, their monetary value and decrease in CO2 emission.
How I built it
We joined Exportin and Top Data Science companies together to build an unique data driven feasibility analysis for heat pump investment.
We utilised existing exportin.io service to combine technologies, electricity and gas prices to heat pump investment feasibility analysis. We built a new Predix application which integrates to any Predix time series data services. Our Predix application then analyses the building operational data, energy consumption and local weather. The analysis includes also simple outlier analysis to spot anomalies on the data. Finally we produce a report to show why building owner should invest on a heat pump and how much it would save money and decrease CO2 emission.
Challenges I ran into
Not enough time series data to build profiling with machine learning.
Accomplishments that I'm proud of
Integrated individual Predix time series data to our Predix application and existing exportin.io service.
What I learned
Learnt how to use Predix time series service and own data science package. Learnt how complex heat pump business scenario analysis is.
What's next for Energy Village People
Expand Predix application with building profiling to analyse buildings energy efficiency and provide deeper analysis of operational data. Support bigger scenarios (multiple buildings) and impact to electricity grid. Expand scenario analysis to other technologies.