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Case Study: Artificial Intelligence for Building Energy Management Systems

Grid Edge

Commercial buildings account for a significant share of global energy consumption. Yet in many commercial buildings, energy is wasted – for example, by providing energy services when buildings are unoccupied. This problem arises because commercial buildings are large, complex systems housing diverse occupants with varying behaviours and needs.

As building energy management systems (BMSs) have to cater to a range of user behaviour, building energy use is not always optimised. Now, as data on building energy use has increased, a wide variety of information is available to optimise BMSs so that they deliver energy services exactly when they are needed.

Concurrently, intermittent sources of renewable energy have grown significantly, creating challenges for grid operators tasked with supplying a steady supply of electricity to the grid. In this environment, matching supply and demand is crucial, and while storage technologies are one solution, tapping into sources of flexible demand is another.

Commercial buildings have the potential to participate in energy markets as sources of flexible demand, reducing their load when required and increasing it when supplies of electricity are plentiful, without having any impact on their operational performance. Doing so would allow building owners to generate additional revenue streams from buyers of their flexible load. However, this requires sophisticated BMSs that allow the building to participate in electricity markets in real-time, and make predictions on energy supply and demand, to ensure that building occupants are largely oblivious to changes in building energy use.

Tapping into the plentiful data that exists to optimise commercial building energy use and allow commercial buildings to participate in markets for flexible demand is now being made possible thanks to machine-based artificial intelligence algorithms.

One of these systems is called “Flex2X”, developed by UK-based company Grid Edge. The system works by combining data obtained from a building’s existing energy management system with other data sources (for example, on weather conditions) and analysing it using artificial intelligence algorithms that can optimise the building’s energy use in real time. The algorithms are considered “artificially intelligent” because they change based on the data they receive, a process referred to as “learning”. This allows the software to make predictions for the building’s energy use, 24 hours in advance, based on its experiences in the past.

The software is also connected to the electricity meter and the wider electricity network. This allows it to monitor the price and generation mix of electricity, and decide when to increase or decrease the electricity load of the building, based on the cost or carbon-intensity of electricity at any given time.

By controlling when the building uses more or less energy, the software converts the building’s electricity load profile from being more or less a fixed load, into a flexible load. Flexible loads are a valuable commodity in today’s energy markets because they help energy market operators to better manage peaks and troughs in demand, and incorporate more intermittent renewable energy sources into the grid.

Grid Edge’s role in the energy system1

Grid Edge’s role in the energy system

This technology could potentially realise a number of benefits for a range of parties.

For building occupants, increasing the intelligence of the building management system should ensure that comfort is optimised and energy services are available when needed while energy costs are reduced thanks to reduced wastage. In addition, occupants interested in questions such as the sustainability of their building’s operations would have access to real-time data on information such as the carbon-intensity of the building’s energy supply.

For building owners/operators, intelligent building management systems like Grid Edge offer the opportunity to reduce costs, cut carbon and maximise comfort through load-shifting and optimisation, as well as recoup the cost of upgrading the building, by selling the building’s flexible load on the market. This could translate to a greater willingness to invest in sustainability upgrades, knowing that the upfront costs of such upgrades could be offset through trading the building’s flexible load.

For grid operators, this technology promises to unlock new, predictable sources of flexible demand, which will help in balancing supply and demand, especially useful as the share of intermittent renewable sources of energy increases.

Measured benefits include:

• Cost savings and revenue generation equivalent to >10% of annual on-site energy costs;
• Carbon reduction through load-shifting and efficiency measures (up to 40% has been evidenced).

Grid Edge has deployed its technology with early-adopter customers throughout the UK and is actively developing partnerships with global energy and building controls companies to scale their technology.

In relation to the energy demand optimisation aspect of the technology, the key barriers are likely to be mistrust by building owners and occupants that the technology can deliver reductions in energy consumption without compromising energy services and comfort.

In relation to the flexible load aspect of the technology, barriers are likely to be regulatory. Energy markets rules must permit the trading of flexible demand at a scale that allows commercial buildings to participate in the market. For example, in some energy markets, the minimum allowable bids for participating are higher than the size of flexible loads likely to be offered by commercial buildings. In addition, some energy markets require access fees for participation, which might pose a barrier to entry for small-scale participants.

Notes and references
  1. Scott, J, Grid Edge: Artificial Intelligence for Energy Systems, Presentation delivered at International Energy Agency Workshop on Modernising Energy Efficiency through Digitalisation, Paris, 27 March 2019