Energy Management/Energy Efficiency

Big Data + Energy = Big Energy Data: Key Trends and Implications


Over 78% of global energy leaders believe analysis of big energy data is important in improving energy management, however the challenge clients face is what to do  with the big energy data, how to make it actionable and drive results that improve business performance. Let’s look at the four key trends and implications below.

# 1. Three V’S: Volume, Velocity and Veracity

 Volume: There is a Data Tsunami today, just since 2008 we have seen 28 million smart meters installed with 1000 times increase in volume of data from before. But data is coming not just from meters but from other intelligent devices ranging from BAS, sensors, thermostats etc.

 Velocity of Data: With more devices we are also seeing data being captured in increased frequency ranging from 5 min /15 minutes to monthly intervals. But why is this data being captured with increased frequency?, so it can enable efforts around smart grid, demand response or Micro-grids amongst other reasons.

 Veracity of Data: More focus being placed on data quality with 75% of companies reporting data quality issues. Poor data quality can cost you through inaccurate billing, missed real time DR opportunities.


# 2. Always Connected, Any time, Anywhere

 With data storage costs reducing over 800 fold over the last 10 years you are seeing an advent of cloud based services and solutions wherein energy data is being stored in the cloud and being accessed from anywhere, anytime through remote mobile devices. Whilst this creates a great opportunity, its implications need to be understood from a client perspective in the context of security of data, latency and response times.

 Intelligent energy devices usage and adoption has also been driven by reduced cost of processing power and chip performance doubling every 18 months (Moore’s Law)  enabling more complex computing operations to be performed at a fraction of the cost before. This has enabled widespread use of intelligent devices enabling you to now get connected to them through the internet.


# 3. Energy Resource and Expertise is not enough to meet demands

The question you might ask is now that I have access to this volume of data what do I do with it to make it insightful and actionable so I can deliver results in terms of increased performance – be it increased energy efficiency or power reliability.

This is a question that many clients are being challenged with. What are the key challenges?

  •  More Energy Engineers needed: It is expected that the market will need 2 X to 4 X energy engineers from what they have today. In real terms it means we will need to add 1 million jobs in this field by 2020.
  •  Not enough talent pool available: In a survey that was done by the state of California over 60% of clients experienced some to great difficulty identifying and recruiting qualified candidates.
  •  Aging workforce: What has also been observed is that 1/3  of this workforce is over 50 years oldFew engineers enter this field with a pure energy engineering degree or certification. What is most commonly observed is that you typically hire a mechanical or electrical engineer who is then trained in this domain.

 # 4. Transform Energy Data into an Asset

Over 80% of clients said that their data holds strategic value and more clients are moving from tactical to strategic energy management by leveraging energy data as a big lever. 


Implication: So what can you do about these trends and stay ahead of the game?

 I would encourage you to start asking these questions and do a capabilities check.

 Software: Do you have software that can acquire process/ analyze the data and support your volume, velocity and veracity needs?

 Unlock Hidden Value: Understand what options you have around Energy management software and how you can bring in  new levels of automation and optimization leveraging cloud based services.

 Energy Resource and Expertise Dilemma: Do you have dedicated resources to address you needs and if not what plans do you have?

References: Multiple sources were used including: Verdantix, Report on Big Data by IBM: National Energy Action Plan, Gartner Group, GihOm-2013, Navigant Research, New Journal of Physics-2009, LBNL Energy Efficiency Services Sector study-2012

13 Responses to “Big Data + Energy = Big Energy Data: Key Trends and Implications”

  1. Ken Williamson Sr

    Very good article, one of the issues is that the majority of young people just want to use the data rather than help mange it. The mature workforce knows that a great deal of work lies ahead to insure the data is properly controlled. Just my $0.02 worth.

  2. vibhu goyal

    yes, we have a software SAS (Statistical Analysis System) through which we can acquire the process, analyze the data and thereafter provide the practicable solution and that too with any volume of data with veracity. It can deliver reports in terms of increased performance once we analyze the data with proficiency.

    • Pankaj Lal Pankaj Lal

      Thanks for sharing your experience. The magic occurs when you combine Big Data with Energy Management software and then apply human expertise to leverage the data to make it meaningful to your business.
      Pankaj Lal

  3. Philippe

    Big Data and Energy Management both sound to be Big Buzz words… but actually this has been proven to be a very efficient combination. Using advanced and predictive analytics to cross correlate variability of operations with variability of energy efficiency can help industries to save millions… without doing any equipment investments. We have successfully applied this approach on several industrial sites. Challenges of this approach are clearly more on people, change management, availability of structured data and availability of people in plants.


    • Pankaj Lal Pankaj Lal

      I am glad to hear your perspective, I truly believe Big Energy Data is a Big enabler for businesses . I am interesting in hearing what you have seen that has worked from a people dynamic perspective.
      Pankaj Lal

  4. Noman khan

    Can you plz tell me some short courses/trainings in the field of big data management, which are offered online. I have done MBA and have good command over spss. I am looking forward to some strategic courses in data management.

  5. Vilnis Vesma

    Start by reducing it to small data, for example by assessment of weekly totals as your first line of defence. Add driving-factor data to the mix so that you can develop a suite of performance models that tell you how much you should have used in each consumption streams under the prevailing conditions. As soon as you do that you can calculate deviations from “correct” consumption, price them up, and rank them in descending order. This “overspend league table” shows you instantly, at the top of page 1, where your costliest exceptions are and what they are costing you. There are few organisations drowning in data who cannot thus compress all their monitoring into one minute a week or less, spending more time delving into the detail only if something significant is flagged up. The problem with this 25-year-old technique is that simplicity doesn’t sell software.

  6. Deon Pretorius

    Fantastic article – making sense of collected data to correctly influence change and efficiency

    • Pankaj Lal Pankaj Lal

      Thanks for your kind words and I am glad to hear this piece resonates with you,I expect to write some followup pieces based on the great feedback and questions I have received as a result of this blog. Pankaj


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