Here is the third post in a blog series focusing on innovations across various markets leveraging the Internet of Things. You can also view the previous post on the Fleet Management industry or on Healthcare.
For a long time, getting an automated energy management system at home was only accessible to those who were ready to acquire a very customized and expensive solution. And still, the capabilities were pretty limited. But in the last decade, the emergence of IoT technologies has driven the development of retail products so strongly that many connected devices have now found their way into our homes.
The smart home space even received an exceptional publicity when Google acquired Nest in 2014 for a record-breaking price. Today even if Nest is only one of the many connect smart thermostats that help us keeping warm and saving some money, it is still viewed as the front display of a market that is undergoing incredible growth.
Just as the smart home space is experiencing tremendous customer adoption, it seems that we’re about to reach the next stage. Why just save a few dollars from your AC / heating system, when the potential for savings is much bigger if you could manage your home appliances more effectively?
That’s exactly what the next phase of home energy efficiency management is aiming at. And this next phase is upon us, thanks to energy data disaggregation.
What Does Energy Data Disaggregation Mean?
Mmmmm… It’s not exactly the perfect marketing buzzword and I’m sure you are probably wondering what energy data disaggregation is about.
Well, in theory, it is pretty simple. Energy data disaggregation aims at dismantling or separating aggregate information (here, the data related to the energy or power consumed across all your appliances at home) by means of data analytics techniques. The goal is to extract insights from this data about the energy used by each of your appliances. Since our fridge, AC, dryer, etc. have all different and specific power consumption patterns, we can (in theory) identify the consumption of each of them at home.
By now, you are probably wondering if we’re talking about ‘data analytics’ because that’s when the simple becomes much harder in practice.
You are right, but several companies claim already to have successfully cracked the problem and have already brought solutions to the market. They follow two different approaches to collect the data:
Approach #1: Collect Energy Data from Other Sources
Probably the most ambitious one is being pursued by Bidgely. Bidgely’s goal is to provide information about appliances, user behaviors, or even power efficiency of a building without the need for any device setup in the house. Here, data used for disaggregation and power consumption analysis originates from two sources: the smart grid itself (AMI or Advanced Metering Infrastructure data) and from the ‘green button’ (i.e. the downloadable personalized energy data made available to their customers by some utilities on their website).
It seems, however, that this approach might have some limitations. Firstly, the data readings are only available on a daily or, at best, hourly basis. This limits the ability of the system to provide the customer with real-time notifications. Secondly, disaggregation results based on data collected from the smart grid might not be as accurate as when the data is extracted directly from the customer meter.
Approach #2: Collect Energy Data Directly
Take the Vancouver-based start-up Neurio for example. Neurio developed what they call the Neurio Sensor that can be installed in the breaker panel. Energy data is obtained from the device that measures (through differential voltage and current signals) the power and energy every split second and wirelessly - through your home WiFi network - connect to the Neurio servers to process the data. Thanks to this data, Neurio claims to be able to identify the power consumption over time of any appliance in the home. You can know pretty much instantaneously if the AC is still on after you left the house, or if an appliance is consuming more than it should be.
But what also makes Neurio particularly interested is that it goes one step further to allow for automated actions in the house. Applications can therefore by triggered by appliance events (e.g. AC on/off) or user actions (e.g. first person arriving home) detected in real-time. For example, as the first person arrives in the office in the morning, the coffee maker automatically turns on. Who doesn’t want to have fresh coffee ready made without effort in the morning!
The Future: A Smarter Home
Think about all the use cases that can be brought up with these types of solutions. Suddenly you have real-time access to the power consumption of any appliance in your house. Obviously you can monitor your energy spend more closely and get information about how much money that new fridge is saving, or when the AC will need to be replaced. But you can also be informed anywhere when your kids got back from school, when they have turned on the TV, or even get a notification as soon as the clothes washing machine is done cleaning your clothes. You might no longer forget that your clothes are in the washing machine all night any more, and in a way, your home might be a bit smarter.