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Domestic Behaviour and Demand: What Can Be Learned from Analysis of Smart Meter Data and How This May Be Useful?

Grindrod, P (University of Reading)
Tuesday 23 April 2013, 14:00-14:30

Seminar Room 1, Newton Institute


We take a careful look at the variation in demand patterns form smart meter data and show that while these may be grouped in an unsupervised manor, such data driven classes are very poorly correlated to socio-economic and asset data. We are not like our neighbours!. We also consider the problem of forecasting smart meter behavior for individual households or small groups of households at substation level. These profiles are very spiked and the law of large numbers cannot rescue the situation for the substations. Thus we consider a methodology for producing rolling forecasts for such data. With smart storage such forecasts could be valuable to the consumer on their side of the meter and the distribution network operator on the other side of the meter. Realistic peaks are required which in turn effects the definition of forecast errors to be minimized: and indeed errors from forecasting peaks earlier are certainly better than errors from forecasting peaks later. We consider some implications for customers and their take up of new technologies, time of day tariffs, and forward planning.


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