How artificial intelligence reduces energy consumption in commercial buildings

Up to 30 per cent of energy consumption in commercial buildings is wasted. But artificial intelligence has made it easier to identify energy waste and determine the causes.

How artificial intelligence reduces energy consumption in commercial buildings.

Up to 30 per cent of energy consumption in commercial buildings is wasted. But artificial intelligence has made it easier to identify energy waste and determine the causes.

Energy consumption from commercial buildings is the biggest climate culprit globally. According to the International Energy Agency (IEA), energy efficiency is the single biggest contributor to lowering our CO2 emissions to meet our climate targets in 2030 as well as 2050.

There's just one problem: every building is unique. Hence, finding energy savings requires a physical audit. This is not only expensive and time-consuming but the snapshot also doesn't necessarily capture out-of-hours consumption, which is a huge problem in itself.

Faster than manual methods: using AI to identify energy savings.

But physical building audits and calculations on theoretical energy savings should be a thing of the past. Technological developments – such as remotely read energy meters and artificial intelligence – mean that we can digitise and automate many parts of the energy optimisation process.

We need to use these new tools if we want to achieve our goal of a green transition in the construction sector. Just as an AI can learn to drive a car, the technology can also be trained to identify and document energy savings in commercial buildings. For building owners, the results quickly add value, for example by eliminating energy waste.

Artificial intelligence is not intended to replace professionals. It is meant to help them identify energy savings that would otherwise go unnoticed or take a long time to identify using the old-fashioned method. With digitalisation, energy savings can be better identified and prioritised. By this, action can be taken where the impact is greatest.

Identifying energy waste is the first step in reducing CO2 emissions.

Many private individuals are now familiar with the problem of food waste. Similarly, we can talk about wasted energy – kilowatt hours produced and distributed but of no use to the end user. In commercial buildings, energy waste is a huge problem. However, it is not talked about very much because historically it has been difficult to identify.

For example, things often go wrong with the control of ventilation systems. Do they actually switch off when the last employee has left? This is where artificial intelligence comes in, as it can identify irregularities in consumption much faster and better than humans.

Building automation is often so inefficient that a study by the US university UC Berkeley estimates that between 10 and 30 per cent of all energy consumption in commercial buildings is wasted. The US results are broadly similar to what has been found when examining the energy consumption of Danish buildings. For example, researchers at the Technical University of Denmark (DTU) have also found large savings on Danish properties with artificial intelligence.

Danish Arbejdernes Landsbank is one of many building owners using AI.

However, energy savings and CO2 reduction only come when the analyses are translated into action. So energy and operational managers need to take over at this point. Depending on the building owner's internal resources and competences, the implementation part can be done in collaboration with energy advisors or technicians. With the building owner's consent, those can also use the tool.

Arbejdernes Landsbank is one of the Danish companies using artificial intelligence to reduce consumption. Here, the tool – in collaboration with dedicated operational staff – has reduced electricity consumption by just over 16% across the entire building portfolio.

Energy savings can be documented automatically.

With deep understanding of energy consumption from the AI, the energy savings can also be automatically documented. Just like "old-fashioned" degree-day correction at the monthly level, the artificial intelligence automatically takes into account 15 different variables every hour. For example, it understands the impact of weather, holidays and the effect of the coronavirus lockdown on the building.

Ento’s AI-powered solution is currently used in commercial buildings such as offices, banks, retail chains and public buildings such as day care centres, nursing homes and primary schools. For building owners, no new hardware is required to get started with the tool.

Although most building owners feel they have a good handle on their energy consumption, artificial intelligence has yet to "meet" a building owner whose energy consumption cannot be improved.


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