Commercial, Energy Efficiency, Industrial  -  March 22, 2019 - By Sukhjinder Singh

Energy and facility managers deserve access to modern technology

Why is it that many C&I energy or facility managers who want to find simple answers from their data seemingly can’t? Across commercial and industrial sites, facility and utility data simply hasn’t been optimized to enable Watson- or Siri-styled queries. Where are the resources enabling facility managers to quickly find an optimized energy efficiency budget or calculate energy use per square foot?

If you were to ask “Hey Siri, How long to get home?” a cellphone can give you that answer in that ubiquitously dulcet tone: “Traffic to home is heavy, so I’m estimating an hour via 95 North.” The exchange takes maybe 10 seconds on a bad day.

It’s a seemingly simple question, but to answer it, a vast assortment of technology is engaged, leveraging searches across disparate data sources: map data comparing all possible routes, highway data disclosing speed limits on each identified path; traffic data (through a mish-mash of Caltrans and Waze alerts) displaying areas of congestion due to accidents or construction alerts.  Siri’s search protocols marshal all this data and provide a useful and actionable answer in a pinch.

Energy data is complicated and hard to manage
The Energy space has become markedly more complex over the past two decades. With the demands of users evolving and grid dynamics changing in our highly competitive market, bills include more data than our grandparents ever saw. Their statements were simpler, with fewer line items and costs remaining nearly constant over any given billing cycle.  Today, some statements contain pages of information phrased in such a way that it all feels like you’d need to have a Ph.D. to decipher it.

The headache only gets worse for site managers with multiple locations served by different utilities. Add to that retail energy suppliers with their own charges compared to rates from third-party solar options. And then throw in the heating and cooling dynamics of different building types -- each having its own tariffs, terminologies, meter read cycles and billing formats. Those responsible for understanding energy costs and trying to find opportunities to optimize or enable efficiency find each incoming bill to be its own data nightmare. 

It’s almost impossible to both assemble the necessary and available data and then process it into the usable insights companies need to make informed Energy decisions.

There is a lack of targeted resources for solving this chaotic mess of data.  Although the energy spend for most C&I customers is their third highest cost, many energy managers face growing pressure to do more with less.  Some try to build or re-purpose existing systems to meet their need but find themselves facing the same onslaught of information to puzzle-out each quarterly statement. It’s a heavy lift with many spending significant amounts of time, effort, and stress with little to show for it. 

So where do we go from here?
We may not have access to those coveted 10-second answers just yet, but Watson- and Siri-styled artificial intelligence (AI) is already being successfully applied in some sectors of the Energy economy.  Why not leverage such AI technologies for the C&I customer?

What has been missing to date is an integrated solution with all energy data being managed via a single, comprehensive, and integrated intelligence platform tailored for the end-consumer. 

Much like IBM’s Watson, such a platform would do all the heavy lifting, be easy to interact with, and enable users to address energy data complexity by performing a number of tasks:

  • Do the work of integrating multiple data sources flawlessly. The right solution should rapidly, intelligently, and accurately combine high quality and granular data related to the energy footprint (e.g., bill information, meter data, mapped building data, relevant lifecycle inputs) with external information (e.g., weather and tariff data) to create meaningful insights.
  • Provide contextual responses including metrics tailored to the user. If a hotel manager wants to know energy cost per bed, while the hotel custodian needs to know how soon until a bulb needs replacing, each can find an answer to their unique question in seconds. 
  • Enable levels of engagement that fit into the routine and needs of the user. Commercial and industrial customers should be able to ‘converse’ with their data and ask numerous questions at any time and have a system that proactively monitors on their behalf. The human-machine interface must be intuitive and easy to use, yet prioritize information so managers can quickly understand a crisis when it hits.
  • Proactively monitoring the corporate energy ecosystem and highlight anomalies and insights that managers should be aware of, without overwhelming them. The right system would also engage machine learning to understand the needs of the buildings, constantly refining and updating its queries and advice based on feedback.

 A holistic approach to managing energy data is both necessary and achievable.  It’s time to stop settling for that inevitable migraine of staring at each billing statement and tackle the issue of energy management head-on. With the rapid deployment of new iterations of artificial intelligence and machine learning, we can get a glimpse of what the optimal solution for energy data management looks like.

Customers – from hotel chains to communications companies to retailers - are finally beginning to reap the benefits as they migrate to the other side of the AI digital divide. The lessons gleaned from other highly digitalized industries can provide insight concerning potential solutions for facility managers. In the near future, C&I managers should expect to enjoy a full suite of easy-to-use tools that can solve problems and quickly answer complex questions, just as Siri confidently and effortlessly directs you on your way home.

 

Sukhjinder Singh, CEO of Pear.ai, brings his expertise in business development and M&A.  He has spend the last 15 years as a serial entrepreneur. By bringing the right people together he has been able to create multiple successful businesses in various industry sectors.

 

 

 

Tags: Pear.ai

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