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Utilising Artificial Intelligence

Often when businesses describe their software as being enabled or enhanced by artificial intelligence (AI), they are taking refuge in poetic license. As anyone knows who has reviewed the literature, we have had specific or “brittle” AI for some time: there is hardly a board game left where humans can outperform machines. But we are a long way from the general AI future that films and novels have predicted for us.

In the half-way house of the present, more specific terms should be used. Machine learning, for example, computer vision, and facial recognition have all gained traction across industries, and in this context artificial intelligence applications will be an increasingly vital aspect of most businesses, and especially IoT businesses, going forward.

As data gathering becomes more sophisticated, and especially as we see the widespread adoption of sensor technology with its enormous quantities of data harvesting, processing, and analytics, it will be a necessity to have some level of machine learning capacity. As Richard Soley, Executive Director of the Industrial Internet Consortium, puts it, “Anything that’s generating large amounts of data is going to use AI because that’s the only way that you can possibly do it.”[1]

In order to optimise energy use in a building, for example, overall energy consumption must be reduced, requiring a careful balance between demand and supply, and this in turn requires the deployment of a number of sensors. In the complex installation of a new building, these sensors will typically be used to monitor occupancy, ambient conditions, and the movement of people around the structure, as well as energy consuming assets like fans and HVAC systems. All of these are integral to the smooth operation of a building, all represent a potential energy saving, and all produce large amounts of data that has to be turned – using data analytics – into meaningful, actionable insight.

Source EY: https://www.ey.com/en_gl/advisory/six-habits-of-digital-transformation-leaders

Obviously then, the definition of a ‘net zero building’ (as outlined in the UK GBC framework), is one that uses machine learning to derive actionable data on energy consumption, among other things, to ensure efficiency. As we move closer to the government targets of net zero (2050 in the UK, 2045 in Scotland), these measurements will become increasingly common, and perhaps mandatory, in order to meet interim targets for power in the buildings sector.

As the move towards industry internet of things continues (and as the requirements for carbon reductions continue to dovetail neatly with requirements for energy cost savings), the development of data analytics capacity will be an essential investment for all businesses. As a recent EY report on the “Six habits of digital transformation leaders” shows, although fewer companies have invested in AI capabilities than in, for example, cloud computing, the return on investment for those who have represents a considerable energy cost saving (see the graph above). As Beatriz Sanz Sáiz, Global Advisory Data and Analytics Leader at EY says in summary, “The future is about embedding AI in core processes.”

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