Collecting Business Value from Energy Data

Australia, Canada, Italy, Japan, Netherlands, New Zealand, the Nordic Countries, Spain, the U.K., and the U.S. all use them, and in some instances mandate them. The smart meter movement has spread around the globe. The trouble is, the reams of energy data that they produce and store are often inaccessible or indecipherable to energy managers.

Even when environmental goals are clear, the mountain of raw data from smart meters isn’t easy to translate into action. Managers carry an extra burden when it comes to realizing energy data’s latent value — they have to decipher a data stream of mind-numbing complexity, and until recently, they haven’t had many tools for the task.

Building energy analytics startup Pilio is one of a growing number of businesses that help other companies manage their electricity, gas, oil and water usage with new data-intensive processes. CEO and cofounder Catherine Bottrill runs Pilio, which emerged from an energy demand-reduction research project in Oxford University.

Along with Pilio cofounder Russell Layberry, is combining behavioral science with energy analytics to help organizations better understand — and manage — their energy use. The heady goal with Pilio: to develop ideas and practices that will shift society toward greater environmental consciousness. “Environmental change,” Bottrill says, “needs to come from people, and people must have the wherewithal and motivation to change.”

Catherine Bottrill talks with Renee Boucher Ferguson, MIT Sloan Management Review contributing editor, about the state of energy analytics — and how to get better at deriving business value from energy data.

Can you give me an overview of what Pilio does?

Sure. Pilio is a building energy-monitoring and management software created with Dr. Russell Layberry from research out of the University of Oxford. We have now spun out of the University to take our innovations to marketing. We are developing a range of products for improving energy efficiency — iMeasure for online home energy monitoring, wMeasure for weather data to do energy analysis. And we’re doing some development work for data analytics on driving energy efficiency and savings.

How is it that Pilio is a spinout from Oxford?

The energy research group that we’re a part of is focused on energy demand reduction — i.e., how to use less energy, especially in the built environment. For researchers working in demand reduction, there is a real shortage of access to good-quality data from which to understand trends in energy use and how industrialized countries like the U.K. might reduce carbon emissions by 80% by 2050.

Russell is an energy modeler wanting to create a comprehensive model of the built environment and then put in different assumptions on technology, policy and behavior to explore how this changes energy consumption. For my part, I am a behavioral change scientist, and what really interested me was how Internet technology can be deployed to support behavior change. So Russell and I married the need for energy data with creating engaging tools and resources for energy consumers.

It started as a research project to create a new dataset, but as the numbers and opportunities grew, it went beyond a research project. And to get the investment needed to take the software platform forward, the logical [step] was to spinout from the University. Oxford University had set up a software incubator, because they realized that IP for the 21st century is in software. We graduated the incubator and incorporated in 2011.

Where does your background in behavioral science fit in with energy analytics?

A philosophy that Russell and I have is that every kilowatt-hour used has a relationship to a person. And there’s got to be a sense of ownership over those kilowatt-hours. We have to engage people in making changes in how they use energy in their building and in their travel.

People need information, but it needs to be contextualized and relevant to them. The analytics part is about creating meaning from their data — highlighting when they’ve got poor energy use and when they’ve got efficient energy use, and helping them understand what actions resulted in those two different outcomes.

In the energy field, we have focused a lot on the engineering solutions or supply-side solutions. But what you often find is, if you don’t think about the person, you’re not going to get desirable, energy-saving outcomes.

What types of data do you put together to create insights for your customers?

For example, we marry together local weather data with energy use to provide energy efficiency feedback. To know how hard your heating system is working or how hard your air conditioning system is working, you’ve got to factor in the weather, because it’s a major driver of your energy use. People appreciate weather is an important factor to take into consideration, but many facility managers do not know how to make full use of this analysis.

What approach do you take in helping people to be more comfortable with data, and to have them look at data more effectively?

Quite typically in building energy management, many people are relying a spreadsheet to monitor energy use. This is not a very good way to make data visible or share results. Through using an online software platform with the dashboard and results sections, people can more easily share energy performance. We aim to take the heavy lifting out of data management.

With my behavior science hat on, I am interested to explore data visualization options for more effectively engaging people in energy use and getting away from straight bar, line and pie charts — data analytics gives the information core that then needs to be visualized.

Is that part of your mission, or your mandate — to help organizations determine how to operationalize data or figure out what to do with it?

Definitely. One of the issues in energy management and efficiency is, nobody is doing it very well. I see it a lot in my field: people investing in hardware to gather the data, but not sure what to do with the data.

We know energy efficiency can save up to 40%, just by running buildings better. So without actually necessarily spending a lot of money on more capital infrastructure — and this is where data analytics comes in — if you can understand your energy use, you can start making informed decisions to reduce energy use. And it is about putting the information in the view of the person who can take action. For example, it is not uncommon to see heating and cooling systems working at the same time. Data analytics can alert anomalies, but the person needs to know what to look for.

With organizations investing so much in data collection technology — particularly with smart meters — why haven’t they gotten to the point that they can understand what the data means?

The structure of what’s happened in energy is that you’ve got companies that sell the data collectors. So they’re selling the smart meters, but they’re not that interested in the analytics software. They are primarily making their money from selling hardware solutions. They want to install it and walk away. I had one client who spent over £100,000 on data collectors, and because no one knew how to look at the reports generated, they didn’t realize that they weren’t even installed correctly. The kit goes in, but it’s not validated. As a result, they have not seen a return on investment.

I am not sure about the protocols and standards are in the United States for smart meters to make transferability and access of data flexible, but this is a live debate in the U.K. What you find is they can’t talk — there’s not a standard format. So if you put in your data collector and you change suppliers, your new equipment might not talk to the old piece of kit. Or you don’t have very clear ownership. You own your data, but sometimes these companies can make it very difficult for you to access your data in anything that’s a usable, flexible format. So, people feel quite stuck.

How do you solve those issues of not being able to access or read data?

You push. For example, a hotel that we are working with, they’re a big enough client that they’re able to push their supplier to give them full data access.

Our main goal is to give people the right tools to manage their energy consumption. We have developed some prototypes for retrofitting old gas and electricity meters to automatically collect energy data and feed it into our software — removing the barrier of manual meter readings or difficult in data access if they have automatic meter readers.

You mentioned that in the U.K. that there is some energy legislation going into place? Can you talk about that?

Here in the U.K., the previous government passed the Climate Change Act, which has committed the U.K. to reduce emissions by 80% by 2050. So it’s supposed to have carbon budgets published every five years setting out how the government is going to progress in meeting its budget.

There are schemes such as the Carbon Reduction Commitment, which is effectively a carbon tax on business energy users. These organizations want to hear, “[This will] save me money.” And we say, “To do that well, you’ve got to know about your energy use.” This is where data analytics comes into play as you can make informed decisions and future-proof exposure to energy price volatility or legislation. But climate legislation, at the moment, is mostly an irritant to companies, rather than being at such a high cost that they are really motivated to make energy-saving investment beyond the low-hanging fruit.

Where do you see the future going with energy analytics?

Energy analytics is core for effectively managing and reducing energy use. With the technology making it possible to gather, communicate and process large data sets, it will be possible to give people precise and actionable feedback. Analytics provides people with insights about their own energy patterns and, where data is available, puts it in reference to others.

For example, we can give our customers feedback on their real-time electricity use and can overlay this with average electricity use curves for a similar building. Customers can then see how their energy-efficiency performance compares to others’. There is much scope for expanding energy analytics as the data to perform analytics becomes more accessible and the techniques for processing and visualizing the data are improved.

The Church of England, [which] we are working with, is a great example. When I started, only a very small number of churches were doing any regular energy monitoring, and this information was not being pooled together to give a fuller understanding of energy use. There are now hundreds of churches feeding data into Pilio each week. This information is being aggregated together and with the sample of users, it has been possible to calculate a total carbon footprint, know the proportion of energy use by building type and calculate an average efficiency comparison. Furthermore, the Church has an evidence base to monitor if they are measurably reducing their carbon footprint in alignment with their commitment to climate responsibility.