Utility Week

In particular, Kelloway says machine learning could also help in predicting the demand for charging. "If the machine learning is aware of bus timetables and traffic conditions, this may assist in being super-efficient at predicting the associated charging demand," he adds. Using real-time weather data to forecast electricity with AI-driven systems will also play an increasingly large role in the grid of tomorrow, says Kevan Mossman, transformation director at the Odgers Interim Network. "It will also enable them to incorporate decentralised energy generation and battery technology into the grid and anticipate how this will impact demand," says Mossman. "At the same time, AI can run real-time analysis of a company's grid and provide a working model of where and when parts of it are likely to be impacted by natural disasters or peaks in consumer behaviour. "For example, it could tell you when a storm is due, the catchments it will hit, which pumps will be affected and whether there are any other parts of the grid that can be made available. is will enable companies to manage energy costs through spot price management." e concept of microgrids and virtual power plants is still in its infancy, but this is another area in which AI will play a leading part in managing supply and demand, as well as empowering local residents to trade their unwanted electricity. In April, Moixa announced plans to create a virtual power plant in Sussex, as part of a scheme that could potentially save the country up to £32 billion. Moixa's GridShare platform will aggregate more than 1MW of spare capacity from batteries in homes, schools and council offices, providing a range of services to National Grid, energy companies and energy distribution networks. e platform will use machine learning and artificial intelligence to tailor its performance to customers' needs and maximise their savings, and this is expected to cut home energy bills by up to 40 per cent. A I h a s i t s l i m i t a t i o n s But Mossman warns that these systems will require large data sets to be fed through machine learning systems – something he adds that utility companies have historically been hesitant about introducing for fear of "losing control". Rob Richardson, who heads up the data science work at Habitat Energy, which has developed an optimisation and trading platform for grid-scale battery storage, adds that data inefficiency is a big limiting factor in how AI will be used to develop a smart grid. "In principle, AI can solve any problem, provided it's fed enough data and given enough time to train and interact with POWER TO THE PEOPLE An automated and smart grid is good news for consumers looking to reduce their bills, according to Moixa Technology's chief technology officer, Chris Wright. e British smart energy firm has been working with the Japanese trading house ITOCHU to provide the AI 'brains' behind its 10kWh SmartStar Energy Storage System (ESS). e ITOCHU system uses Moixa's GridShare Client machine learning service to understand how the storage system is operating and provide customers with live data and regular feedback on their energy flow and savings. Wright says 1,300 to 1,500 of these storage systems are now being rolled out in Japanese homes every month. "We are seeing the cost of energy decrease by significant amounts," he explains. "On average, we are showing that we can achieve a reduction of between 40 and 50 per cent compared to the standard behaviour of the ESS as our Japanese customers come out of the feed-in tariff. "By predicting how those homes will consume energy for the next day, you can plan ahead. For example, you could make that home available for a flexibility service in the afternoon, when usage is low. "We use machine learning to learn about customers one by one," adds Wright. "So, while some energy companies might use average statistics to predict activity, we collect consumption data from homes every 30 seconds. We understand how they use energy and we understand how they generate energy. " en we can take that information and combine it with weather forecasting data. We then apply other algorithms and generate a prediction of what that household will do over the next 36 hours. We can then generate an optimum plan for that household over that time period. " e AI is standing in the place of the consumer. is technology is working for the consumer, without them having to make any interventions." Germany went from 1,000 generators in 2000 to a recently estimated 1.5 million 1,000 33 ISSUE 03 MAY/2019

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