| | NOVEMBER 20209E ERGYTech Reviewmanaged by AI, creating opportunities in demand side response and optimising how we use the power grid. Meanwhile, 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 site performance, deal with potential equipment failures before they happen, and reduce energy inefficiencies. Beyond developing new products and solutions for our customers, AI also enabled us to innovate and be more efficient at what we do internally to support our commercial propositions. One of our subsidiaries, Io-Tahoe, which provides solutions that enable organisations to extract meaningful relationships from their own data via machine learning was born out of our own internal needs. 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.Another application we implemented is in the automation of report writing. One of our tasks is to monitor market developments, identify key drivers, and communicate this to other functions for reporting purposes. Historically, this consumed a considerable amount of time. Typically, multiple analysts had to monitor different markets and write up their analyses in their own style, which then required editorial work to consolidate. Using supervised learning as well as other analytical techniques we managed to automate the analysis and produce reports in plain language that describe what happened in a certain market and why. This frees up time for our analysts to focus on complex questions that bots cannot yet tackle. The examples I mentioned 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. Forecasts play a crucial role in the energy sector as a key driver of decision-making in trading and risk management
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