Kieran Greer, Yaxin Bi, "Event-Based Clustering with Energy Data," WSEAS Transactions on Design, Construction, Maintenance, vol. 2, pp. 197-207, 2022
Kieran Greer, Yaxin Bi, "Event-Based Clustering with Energy Data," WSEAS Transactions on Design, Construction, Maintenance, vol. 2, pp. 197-207, 2022
Kieran Greer, Yaxin Bi, "Event-Based Clustering with Energy Data," WSEAS Transactions on Design, Construction, Maintenance, vol. 2, pp. 197-207, 2022
Kieran Greer, Yaxin Bi, "Event-Based Clustering with Energy Data," WSEAS Transactions on Design, Construction, Maintenance, vol. 2, pp. 197-207, 2022
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
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
