Artificial Intelligence and Machine Learning are changing the world of energy

By Nazim Osmancik
Group Head Economics and Fundamentals

Nazim Osmancik on how the recent revolution in the energy sector has been in the digitalisation of data and utilisation of algorithms that can ‘learn’.

Artificial Intelligence (AI) and Machine Learning (ML) are changing the world around us and the energy sector is no exception. Organisations that can harness its  power in the right way and early on will benefit the most.

AI is changing the world around us and the energy sector is no exception. Technology has always played a crucial role in our sector, especially in engineering applications. However, the recent revolution has been in the digitalisation of data and utilisation of algorithms that can ‘learn’. This opened the door to advanced automation and AI applications that cut across sectors.

As with humans, AI needs lots of information and time to learn and master something. As computational capability advanced and the amount of digital data increased, AI started to work, unlocking possibilities that were in the realm of science fiction a decade earlier. AI systems are now routinely deciding on the ads you see on a website and recognising your face to log into phone apps. They can help diagnose medical conditions, increase the output of a dairy farm by identifying what makes animals “happier”, and park your car.  

In the energy sector, and electricity in particular, there were several trends that paved the way for AI to be uniquely valuable. Falling costs of renewable energy, ascent of on-site distributed energy, emergence of IoT, and digitalisation of data disrupted the business models built around large centralised supply. The intermittent nature of renewable generation increased price volatility and created challenges in system operations. In this environment, predicting supply, recognising patterns and responding to them rapidly are key to maximising the value of our flexible generation assets such as grid scale batteries. 

Meanwhile, on the demand side, energy customers have been asking for more insight and control over their energy use. The information they need is available from smart meters and sensors, which are already in their billions and growing rapidly. Centrica alone has sold around 2.5 million smart thermostats and other types of sensors under its Hive brand. Harnessing the information from these devices with AI, supports us in serving the changing needs of our customers. Our Hive thermostats give our residential energy customers complete control over their energy use. Our BoilerIQ system predicts boiler failures before they happen and helps schedule engineer visits to prevent them, so no one wakes up to a cold house. 

In the near future, as electric cars become more commonplace, scheduling charging and alternative uses of the car battery will be managed by AI, creating opportunities in demand side response and optimising how we use the power grid. For businesses, the use of energy data is already well-beyond energy procurement. For our business customers, we have an Energy Insight service that provides intelligence driven by data from self-powered sensors to optimise performance, deal with potential equipment failures before they happen, and reduce energy inefficiencies. 

In my division, forecasting prices and flows play a crucial role as we develop Centrica’s view on energy markets and global macroeconomics for strategic and tactical insight. Machine learning is enabling us to measure, understand, and improve forecasts, not only our own but also those we get from external organisations. When we first applied machine learning techniques to our forecast data we were astonished with the results. We discovered that we had strengths in forecasting certain indicators that we were not aware of and that each forecast had a distinct ‘shelf life’ which helped optimise the time spent on quality control before publication. Most importantly, machine learning helped us improve the forecast performance dramatically by learning from past ‘mistakes’ in a systematic and adaptive manner.

These examples are just the tip of the iceberg in terms of the potential applications of AI and machine learning in making organisations more competitive and more efficient. The key to success will be early adoption and effective use of this technology to train it on data and ‘learn’ as the real value to the organisation is in what is learned, rather than the algorithms which are already well-developed and widely available open source.   

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