InteSys

Project abstract
i-Magine predictive heating controller from InteSys Ltd creates a building energy model on a microchip through machine learning, using readings from its sensors. After an initial learning stage, the model enables the controller to do a short-term prediction of temperatures in a building and consequently it delivers the right amount of heat at all times. This achieves energy saving of at least 20%. A number of competitive technologies, most notably Nest Thermostat, also claim learning capabilities, however these are limited to user set temperatures and times, and therefore merely replicating functionality of conventional devices. How should this controller be positioned on the market in order to be more desirable than the competition? Does it matter what it looks like, considering that it is currently in a kind of ‘boiler suit’ in comparison with ‘smart casual’ competition, such as Nest thermostat (see Figure 1 below)? Is predictive function enough to make it more competitive? What other functions would make it more desirable than the completion? What other things can prediction do for us in other fields?

Skills required
Product Designers

Electronic Engineers

Web/App Developers 