Cloud computing and the associated reductions in the cost of cloud computing have given way to big data. Lots of it – so what? With that has come big data analytics. The days of limited server space and limited rows on a spreadsheet are a thing of the past, giving insight into areas of our everyday that we previously could not capture – so what? We have access to data and the power to analyse the data, but only if we do something with the information does it provide us with any value.
In the not-so-distant past, only a few companies (like Google) saw the power of big data and big data analytics and had the resources to invest in it. Now, the cost of big data makes it much more broadly accessible. Everyone is talking about it and incorporating it into every commercial, residential, and industrial market. Most energy news feeds have many articles talking about big data and cloud computing.
The energy marketplace is also a buzz with big data analytics. But, the missing element is the action that results from big data analytics – big data “actuation” – if you will. This is a bigger nut to crack. Google’s purchase of Nest is validation that using the collected data is where the real benefit is found. The Nest Learning Thermostat is a device that not only collects information about your home comfort heating and cooling needs, but takes that data and uses it to take action for your home heating and cooling comfort.
Even at home, gaining control of your energy consumption requires some effort. And smart home thermostats is certainly one of the best places to start. In a residential setting, one thermostat is relatively easy to manage. I can speak from personal experience with my home thermostat. I have a thermostat that supports scheduling as wells as various fan circulation modes. It also interfaces with the local utility for demand response by cycling the air conditioning compressor, a process which I can control it from my smart phone. It does not have access to the internet of things, but I manually manage free cooling opportunities versus when I need the help from mechanical cooling. (And by free cooling in my home, I mean opening the windows and turning off my central air conditioning. I did not install a plate and frame heat exchanger.) I can log on to see my hourly historical consumption for the previous days. It allows me to understand how I consumed electricity and how I might change my habits, like running the electric clothes dryer outside of peak hours, cooling the house before peak or mid-peak hours occur, or reducing my existing baseload by unplugging devices that I typically just leave plugged in. It takes effort that most people simply do not or cannot set aside.
The same is true for commercial and industrial facilities. The actuation of big data is complex, because it is a multi-variable problem with many different internal and external inputs. Having the data is a start, but doing something with it is a completely exercise.
The residential electricity market is a disperse market that consumes about 1/3rd of our total electrical energy generation in North America. The remaining 2/3rds is nominally split between the commercial and industrial sectors. To enable energy savings through the reduction of waste energy consumption across these sectors, we need to consider three elements: (1)cloud computing delivering the power of endless high resolution data, (2) big data analytics with the ability to provide precise input and feedback, and (3) the internet of things (IoT) to provide the actuation of the the big data analytics to realize the energy waste savings.
How would you roll out the same actuation in a large commercial facility? That is, how do you provide actuation with big data and big data analytics where there are many thousands of inputs and outputs to consider on a minute-to-minute basis? Let’s understand the variables to consider when making energy operation decisions: (1) control system information, (2) building information, and (3) external information. Control system information is all of the data from building automation and management systems. Things like space temperatures or mixed air temperatures and the central cooling plant variables like chilled water supply temperature. Building information variables are those such as the occupancy of particular spaces or the time when a facility will be receiving a large number of people into a space (morning rush or event load-in, for example). External information such as weather data or utility rate structure is another input into the large quantity of big data to consider.
All of this has to be tied together. The information has to be integrated and coordinated with actuation of physical devices in a manner that will take action to save energy cost. It’s not as simple as just providing the analytics from vast amounts of data. Producing the information to take action is the first step, but taking the action in a timely manner is the most critical step. Someone or something has to take a positive energy reduction action to realize energy cost savings. It has to be done in a timely manner as most savings opportunities are time sensitive – weather, utility rates, and occupancy schedules.
Big data is a stepping stone to big data analytics. Big data analytics is a stepping stone to big data actuation. The actuation through automation of big data analytics will provide energy cost reductions.