We live in a world of buildings, in our cities, in our towns. As our global population grows, to almost ten billion by 2050, even more buildings will be needed. Although incredibly important to our day-to-day lives, they come with an environmental impact.

Buildings consume nearly 40 per cent of end-use energy worldwide and are responsible for approximately one-third of total carbon emissions. Operational emissions (from energy used to heat, cool and light buildings) account for approximately 28 per cent of global emissions.

An environmental, economic, social and governance imperative

Sustainability has become a focus for governments and investors globally and if you are a building owner and/or operator you should be considering how to improve the sustainability of your assets from a multi-dimensional perspective:

Environmental – Do you want to reduce your carbon emissions?
Economic – Do you want to decrease your energy costs through a simple, low risk, zero cost way to realise immediate operational savings?
Social – Do you want to improve your carbon footprint and the impact you are making on your community and the world as a whole?
Governance – Are you focused on leadership in your sector and maintaining or gaining competitor advantage and market share through innovation?

Increasing efficiency and decreasing carbon emissions of building systems

In this article, we consider a bold vision for how building owners and operators can create an enviable sustainability brand and establish market leadership by reducing operational carbon emissions and energy costs, noting that 75 per cent of a building’s lifecycle costs are in the operational phase. It’s using machine learning to optimise building systems by considering existing data to predict future scenarios and uncover opportunities for improvement.

The life cycle cost of a building shifts over time.

The life cycle cost of a building shifts over time.

Figure 1. Life-cycle cost of a building. Source: Schneider Electric

While the traditional rules of thumb and building standards may improve building energy efficiency, they don’t necessarily result in optimal operation as they don’t consider the interactions among multiple pieces of equipment. Machine learning and artificial intelligence algorithms can forecast and improve building energy performance.

Lifetime ROI of different approaches to maintenance.

Lifetime ROI of different approaches to maintenance.

Figure 2. Lifetime ROI on different approaches to maintenance.

The machine-learning application considers energy efficiency of existing stock with existing data to demonstrate how the system has historically performed over time. A move to decrease a building’s operational emissions in existing stock requires careful monitoring of energy consumption and building performance.

The first step in optimising building energy consumption is calculating the amount of carbon emissions by using a building energy assessment method. Figure 3 shows how the assessment is separated into four parts. This is an informative method that provides a comparative energy performance index for decision-makers.

Figure 3. Building energy assessment method.

Figure 3. Building energy assessment method.

Figure 3. Building energy assessment method.

Improving energy prediction and analysis through machine learning

Machine learning is generally used to describe a computer algorithm that learns from existing data. Machine learning models discover the relationship between multiple components with regards to a target variable. When the algorithm is trained on enough data, of high enough quality, it can make accurate predictions on the energy consumption and performance of different system control methods.

The multidimensional interactions between system components make this process impractically difficult via traditional analytical means.

There are opportunities for building owners and developers to improve energy prediction and analysis using machine learning. The outputs enable much higher resolution decision-making around investing in building services systems that lower carbon emissions and energy costs. It is the precise application of complex mathematics and building dynamics.

Historical and current building energy data is used to derive accurate energy usage for all building components with internal and external details as the inputs (e.g. climate information, construction fabric, heating and cooling, water).

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