Tapping the right data to cut energy costs.

To start, any large organisation operating across a significant number of properties needs to understand when, where and how it is using energy and then determine where there is wastage. Understanding where to start is easy but capturing and analysing this data can be the real challenge. More than half of the companies we deal with simply don’t have easy access to all the figures they require to get a clear picture of their energy use and spending.

Overcoming the scale and complexity involved can make the challenge even tougher. For example, an organisation operating 100 buildings with, on average, 10 energy metres per site could create over 17.5 million records per year. It would then have to process all that data to get a complete and accurate picture of its energy consumption. A global enterprise might have as many as 55,000 properties worldwide – making the magnitude of the data analysis that much more challenging.

But the news is not all bad. The deployment of Artificial Intelligence (AI)-driven software tools that gather, measure, and analyse immense amounts of energy usage data means that generating actionable insights is not an overwhelming task today. Leverage machine learning capabilities, these tools make turning efficiency and sustainability goals into achievable KPIs much more manageable.

Getting A Grip On The Data

For a business to harness the data it generates across a broad property portfolio, it first needs to determine how to capture it. When you look at a retail store, an office block, a hotel, a shopping centre, a medical facility, or another commercial building, you will find that sensors can be – and in many cases already are – used to track and manage footfall, air quality, desk occupancy, meeting room use, system logins, and other everyday aspects of the building use. These can be used for effective energy management.

The first step is to bring the data all together in a way that that it can be understood. More and more, the way to do this for many organisations is to implement energy management software solutions that automate this data collection, feeding it into a user-friendly application for analysis and reporting. Employing AI-powered analytics is critical to making sense of the massive amounts of complex energy data being processed.

Taking a data-driven approach in this way enables many organisations to identify and combat common and often unnoticed energy wasters across their property portfolios, giving them complete visibility of power consumption and ways to boost efficiency.

How To Realise Energy Effciency

Working with organisations to tackle energy inefficiency this way, we have been able to identify ways in which energy efficiency can be improved – sometimes through obvious actions on issues that went unnoticed. For example, one retailer tracking and analysing energy consumption discovered that it had been leaving on escalators connecting its four floors through nights and weekends for five years.

No company can afford to ignore this type of wastage today –something most management are well aware of. An increasing number of UK organisations see managing their energy consumption as a major concern. Nearly two-thirds of British businesses (64%) say energy is now their top business risk, with 91% saying their board is concerned about how they are dealing with this issue, according to the npower Business Energy Tracker 2023.

Adopting AI-enabled systems to control their energy usage is the route many companies are taking, with most are seeing hugely positive results. For instance, deploying energy management technology enabled Carlsberg UK to reduce power use in its brewing process by 10% – while cutting its water consumption by 10% and its effluent costs by 16%. A leading European real estate and facilities management company, Apleona, deployed a centralised system to report on carbon emissions while identifying energy conservation measures – reducing, in a typical project, consumption by 25%.

Taking Stock Of Compliance

AI is also helping organisations to better position themselves to comply with sustainability standards across their lease and property portfolios. For many companies operating across large property portfolios, manual ‘lease abstraction’ – extracting and making sense of large amounts of data from multiple, sometimes hundreds or even thousands of leases – can be costly and time-consuming. Often it can take too many staff hours to be costeffective and too long to get the information the companies need by the time they can use it.

Deploying AI-enabled contract intelligence solutions that scan and organise data from leases and other documents allows companies to quickly gather and evaluate information thousands of contracts that are often hundreds of pages long. In one example, a managing agent handling over 2,000 leases was able to build a picture of environmental practice and compliance across all the properties it manages using an AI-powered contact intelligence solution.

The company was able to benchmark its properties in areas such as energy rating and performance, waste handling and facilities, the use of sustainable materials, and other elements relating to environmentally friendly practices to create a ‘green scorecard’ for each property it manages. The result has been to enable the managing agent to address environmental sustainability issues as and when new lease negotiations hav come up.

Clarity With Real Savings

Energy management is becoming more strategic and centralised among businesses of all types and sizes as they tap into AI capabilities and analytics tools to monitor, measure, analyse and manage energy consumption. The result is a clearer picture of energy usage that empowers them to make smarter decisions, with energy costs cut by as much as 30%.