A New Use for Autoencoders in the Utility Industry
Learn how WWT experts are leveraging neural network autoencoders and k-means clustering to segment energy consumers based on meter reading data.
Each day, utility companies use smart meters to collect an enormous amount of data on energy usage. In this white paper, you will learn how to cluster customers based on patterns in their electricity usage, as well as be presented with potential service enhancements utility companies may be able to make based on results.
Smart meters used by electric utilities collect an enormous amount of data on energy usage each day. Leveraging this data, we can better understand how individual consumers use their electricity by clustering customers according to their pattern of electricity usage. Clustering, however does not perform well on high-dimensional data such as meter readings. To overcome this, a type of deep neural network called an autoencoder can reduce the dimensionality of the meter reading data to a representative set of relevant patterns. Clustering then can be performed on the lower dimensional, representative data to group together consumers with similar patterns of usage. This clustering is a fully unsupervised technique. In general, this process of reducing dimensionality through auto-encoding and subsequent clustering may be used to characterize a variety of high-dimensional data sources other than electric meter readings.
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