Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Event-Based Clustering with Energy Data

Version 1 : Received: 19 January 2022 / Approved: 20 January 2022 / Online: 20 January 2022 (20:40:54 CET)

How to cite: Greer, K.; Bi, Y. Event-Based Clustering with Energy Data. Preprints 2022, 2022010313 (doi: 10.20944/preprints202201.0313.v1). Greer, K.; Bi, Y. Event-Based Clustering with Energy Data. Preprints 2022, 2022010313 (doi: 10.20944/preprints202201.0313.v1).

Abstract

This paper describes a stochastic clustering architecture that is used in the paper for making predictions over energy data. The design is discrete, localised optimisations based on similarity, followed by a global aggregating layer, which can be compared with the recent random neural network designs, for example. The topic relates to the IDEAS Smart Home Energy Project, where a client-side Artificial Intelligence component can predict energy consumption for appliances. The proposed data model is essentially a look-up table of the key energy bands that each appliance would use. Each band represents a level of consumption by the appliance. This table can replace disaggregation from more complicated methods, usually constructed from probability theory, for example. Results show that the table can accurately disaggregate a single source to a set of appliances, because each appliance has quite a unique energy footprint. As part of predicting energy consumption, the model could possibly reduce costs by 50% and more than that if the proposed schedules are also included. The hyper-grid has been changed to consider rows as single units, making it more tractable. A second case study considers wind power patterns, where the grid optimises over the dataset columns in a self-similar way to the rows, allowing for some level of feature analysis.

Keywords

stochastic clustering; energy prediction; disaggregation

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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