How Data Can Help In The Cost-Of-Living Crisis?

As we all know, knowledge is power, and increasingly industries are realising that decisions grounded in data are better decisions. In retail, consumer behaviour data helps to inform which products are being viewed the most online and in banking, transactional data can be used to identify fraudulent activity. The premise being, the more you know, the more that you can control.

As energy bills and food costs increase the overwhelming message for the individuals and companies facing surging costs is to ensure that you are aware of what is going in and out of your accounts. The logic is that without this knowledge, opportunities for potential savings may be missed.

But other than regularly checking a bank statement, what other information or data techniques can be used to inform decision-making and potentially cut costs for businesses and individuals?


As bills rise, energy is the word on everyone’s lips and technology is constantly developing that will help consumers to better track and control their energy consumption. ‘Smart homes’ for example – the term coined to describe households that have at least two forms of ‘smart technology’ such as smart meters or smart bulbs – are enabling users and suppliers to track household energy consumption and identify where it could become more efficient.

There are now 2.22 million smart homes in the UK, and the increasing digitisation and connectivity of devices, have only made homes smarter, with increasing numbers of household devices that can be monitored and controlled such as smart lights that can be dimmed, or switched on or off.

The Internet of Things (IoT) has enabled sensors and other measurement devices to speak to one another. Digitisation has packaged this into the accessible format of a mobile phone app where devices can be controlled, whilst automation technologies have reduced human error by using sensors to automatically turn off lighting when no one is in the room for example.

For businesses, Energy Management Systems (EMS) are becoming popular. These automation systems collect energy measurement data and make it available to users through graphics, online monitoring tools, and energy quality analysers. These systems can then automatically change the actions of the controlled device and facilitate the use of energy reduction measures, such as putting a device in sleep mode when not needed.

An EMS uses metering sensors that measure energy usage, a control system that transmits commands, and the actual controlled devices, such as air conditioning units, fans, or lights. A good example of a very basic EMS is the thermostat in your house, which has a sensor that measures the temperature in the room and a controller that tells the heater to turn on or off.

As consumers become more energy-savvy, the bank of data surrounding them – habits, consumption etc. is also building. Not only will this allow customers to see a pattern of their habits forming from current and historic data, but also gives opportunities for companies looking to offer competitive rates and more fodder for data analytics processes; think smart data that could allow policymakers to better understand how people use energy and how to reduce their costs.


There has been a huge amount of research into how this data can be used in the predictive modelling space. For instance, predicting the energy consumption of a building will allow owners to better plan ahead around peak times of consumption and it may influence decisions such as how many days you want to have your office open or your energy provider.

An efficient method for predicting electricity consumption in buildings is the use of ‘soft computing’ techniques. Such methods make use of data measured by sensors installed in buildings and inform optimised decisions and actions to save energy. For example, examining how a building’s design characteristics – wall, roof and window materials – are affecting its energy consumption by using sensors to detect heat loss through the roof.

Electricity load forecasting is another important tool – the accurate forecasts of commercial building electricity loads can reduce costs for companies by reducing electricity use around peak demand times.

Some researchers are looking to combine the predictive modelling of energy consumption with others, such as those around behaviour. A recent study looked at how lighting control in office buildings is driven by occupants’ demand for an indoor light environment.

However, due to the effect of glare, lighting control is often associated with shading adjustment. The study proposed a prediction model which can accurately describe the lighting and shading coupling control behaviour by fully considering the difference and diversity of occupants.


The future of using data to decrease costs for consumers and businesses will depend on how companies decide to use data analytics technologies to extract business intelligence going forwards. Businesses and individuals can easily purchase smart technology to monitor and control their data usage but enriching this data with complementary information will give deeper insights that could inform, for instance, the launch of a new service. As a case in point, PG&E has used SmartMeters to collect consumer energy-use data at hourly and daily intervals. The energy consumption data will supplement existing information on customers’ demographics, billings and payments, call centre reports and utility pricing, among other variables.

The company hopes to gain insights into how its SmartMeter platform might be used ‘to engage customers, reduce energy consumption and offer customers appealing alternative pricing schemes.’ Customers who participate in the program will have the ability to be notified by email, text message or phone when their utility use is moving toward a higher-cost tier.

Awareness around the importance of monitoring the energy consumption of your home or business, and the tools that can make it straightforward, needs to improve. And as data analytics continues to inform business intelligence, the energy-saving services yet be offered are no doubt going to be plentiful.

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