The energy sector’s technological transformation

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Talk of clean technology has been widespread recently, with the UK’s new industrial strategy acting as testament to the government’s commitment to embrace new technology in the energy sector.   

In addition to this commitment to embrace new technology is the pledge to remove barriers to smart technologies like battery storage that support plans that are in place to significantly change the way electricity is produced, used and stored. With UK consumers overpaying by a staggering £5.4billion a year on standard tariffs alone, it’s time that the energy sector is redesigned to optimise prices, and put power back in the consumers’ hands. 

As a part of this approach, the government has outlined plans to ensure that all households and business are offered the option to have a smart meter installed. While these smart meters give consumers a greater understanding of their energy consumption, the data made available to them via old fashioned In-Home Displays (IHDs) only scratches the surface when it comes to what is actually possible, and what can have a real impact on how consumers behave. 

While the most advanced smart meters sample energy consumption once every six seconds, the majority provide samples up to once a minute. This means that while they can accurately provide homeowners with the total cost of their energy consumption, they miss out on the opportunity to provide enough detail to encourage the kind of long-term behavioural change that would ultimately lead to lower energy bills. These smart meters also need to be installed by an electrician, often at the inconvenience of consumers who these days are used to plug and play products. 

Central to improving this system and providing consumers with more valuable insights into their home is unlocking the granular data within it. New smart home solutions, such as Verv, sample energy data at over a million times faster than a smart meter, and utilise artificial intelligence to analyse this data and identify the energy consumption of individual appliances throughout the home. These real-time insights about your appliances is what can unlock information that extend far beyond overall consumption costs. 

At such a granular level, energy data is no longer solely about energy consumption and can prove valuable in ensuring safety and security in the home. Perhaps you left your straighteners on by accident while getting ready. With real time data analysis, this kind of system could alert you to go back and switch them off before leaving the house. It could also alert you that your lights had been switched on while you were on holiday, so you could send a neighbour to check on your home and belongings. However, it also extends beyond your immediate safety – what if you could be notified if an elderly relative fails to switch a light on, or use their kettle to make a cup of tea in the morning. 

Applying advanced machine learning techniques to this kind of energy analysis can even tell you when your individual appliances are inefficient, and may be about to break down – letting consumers make more informed decisions about when to replace costly appliances and avoid them returning home to ,  a flood from a broken washing machine or a freezer full of defrosted food. It could even use product data collected from each individual appliance to recommend a similar, but more energy efficient alternative, based on personalised usage habits. With water damage insurance claims alone amounting to £667m in 2016, the ability to predict breakdowns stands to pose a significant benefit to everyday consumers. 

Third parties can also derive added value from this level of data sampling and analysis. With consent from consumers, manufacturers stand to benefit from in-field usage data on their appliances’ performance, allowing them to analyse and improve the long-term performance of their products. Some manufactures may even be willing to pay consumers for access to this data. Data on appliance usage behaviour could even be used to help validate product warranties, validate insurance claims or even to more accurately price home insurance policies and extended warrantee policies. Similar to telematics devices which are installed in cars to inform insurance companies about consumers driving habits, and potential faults with the vehicle, this kind of smart home data can help insurance understand and mitigate risks in the home – allowing them to offer more accurate and competitive pricing structures. 

Looking to the future, beyond smart meters, is the application of blockchain technology which will ultimately facilitate peer to peer energy trading. This kind of technology is already being used in Germany, Australia and Canada to decentralise and democratise the energy markets. This technology facilitates direct peer to peer trading between consumers, allowing one customer with renewable energy storage to sell excess energy onto another consumer who may not have it. Importantly, this will improve access to low carbon electricity to those who cannot currently afford to invest in renewable technologies. 

Blockchain technology is about new opportunities in a variety of sectors, but in the energy sector, combining machine learning techniques with the transaction and authentication capability of blockchain means delivering cheaper and more sustainable energy to consumers is possible. By giving individuals and communities the power to support and control their own energy needs and tackle increasingly challenging issues around energy management, peer-to-peer energy trading could have significant societal benefits for communities as a whole. 

As the economy moves towards a ‘prosumer’ directed economy, it's important that the energy sector keeps pace. Peer to peer energy trading will go a long way in helping to digitise decentralise and democratise the energy sector in the UK, putting consumers back at the centre of the sector, and delivering a long lasting, and positive social impact. 

Peter Davies, Founder and CEO of Verv 

Image Credit: Skeeze / Pixabay