However, apart from a few specific energy-intensive industries, most companies see energy saving as a “nice-to-have”, and not as a top priority.

SwissCognitiveA McKinsey study found some interesting drivers for this phenomenon, the most crucial being:

  1. Need for investments
    In a cash-strapped economy that changes fast, investing large chunks of capital for energy efficiency is not always possible, especially if energy is not part of the business’s core activities.
  2. Difficult to measure
    Energy is one of the few goods/services that we don’t pay for at the moment in which we consume it. This makes it incredibly hard for consumers to identify the drivers of a higher or lower energy bill.
  3. Awareness
    Many people simply do not know all the different options they have to achieve significant energy savings.

Let’s dig deeper into these points and see how Artificial Intelligence can address them.

The traditional way to save energy has always been to invest in new appliances, new machines, or better infrastructure. For instance, a building can become more energy efficient by investing in better insulation or a better HVAC plant. Or a factory could install a CHP (combined heat and power) solution, or change its machinery for more modern and efficient ones. The common denominator here is that all these initiatives require some degree of investment in physical components that are often quite expensive. We don’t discount low-hanging fruit that can bring about substantial gains with limited investment, but these kinds of measures are often already in place. The real challenge is to take it to the next level.

Fortunately we live in a digital world, and developments in Artificial Intelligence could bring about the change we need to achieve reach for the next level up.

Availability of data is the first and most important condition to enable the use of Artificial Intelligence. Sensors have become a commodity, which enabled a big shift to a world of software, and of course the collection and management of big data. Using AI algorithms, and feeding it with the data points we collect from processes, machines or buildings, allows us to create a digital twin that is an accurate representation of where the data was collected from.

Once a digital representation has been created, various optimization solutions can be explored without changing any hardware or infrastructure. Rather, one could simply explore changing the way one uses what is already in place. And it’s been tried at tested. Google famously experimented with this in their data centers, and achieved cost-savings of up to 40%.

The key takeaway here is that, despite tradition, substantial savings can be made without having to invest heavily in new infrastructure. With the right sensors in place, one can turn to software solutions. It is no surprise then that the International Energy Agency’s Digitalization and Energy 2017 Report considers smart thermostats as the energy efficiency invention with the highest potential impact; offering low barriers to digitalisation and a potential energy cut of 10%.[…]

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