ARCHITECTURE & DESIGN
Energy savings through machine learning
Impact investment reduces carbon and cost footprints by George Catto, Client Services Director at AMR DNA, an Energy Assets service
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t’s hard to imagine that just five years ago the suggestion that energy managers develop efficiency goals based on clearly identified and quantified opportunities would at best have been considered naïve. The technology to analyse the huge volumes of energy consumption data needed to spot efficiency opportunities in multi-building portfolios, such as a university or FE campuses, simply wasn’t mature enough to reach firm future-focused
22 | Autumn 2021 | CAMPUS
conclusions. Any such attempt would almost inevitably have been based on opinion and subjectivity. The dimensions of the challenge are clear and one solution might be to recruit a team of ‘all-knowing’, ‘hard-working’, imaginative, smart assistants to make any spreadsheet ‘sit up and beg’. However, the volume of data is such that it would take an army of experts to make sense of it … not to mention a near-infinite budget. This challenging environment for energy managers was illustrated in a
survey conducted during an Energy Assets webinar event which showed that fewer than one in 10 energy managers have capacity to review consumption data more than once a week. At the same time, six in 10 believed that artificial intelligence (AI) and machine learning, could transform the way they analyse and interpret data to improve efficiency. So, what has changed over the last few years? One of the most powerful tools
