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Improving Cluster-based Methods for Usage Anticipation by the Application of Data Transformations

Abstract:

The wide adoption of Internet of Things (IoT) infrastructure in recent years has allowed capturing data from systems that make intensive use of electrical power or consumables typically aiming to create pbkp_redictive models to anticipate a system's demand and to optimize system control, assuring the service while minimizing the overall consumption. Several methods have been presented to perform usage anticipation; one promising approach involves a two step procedure: profiling, which discovers typical usage profiles; and, pbkp_rediction that detects the most likely profile given the current information. However, depending on the problem at hand, the number of observations to characterize a profile can increase greatly, causing high dimensionality, thus complicating the profiling step as the amount of noise and correlated features increase. In addition, the profile detection uncertainty increases, as the cluster intra-variability becomes larger and the distances between the centroids become similar. To overcome the difficulties that a usage profile with high dimensionality poses, we developed a methodology that finds the intrinsic dimensionality of a dataset, containing binary historical usage data, by performing dimensionality reductions to improve the profiling step. Then, the profile detection step makes use of the transformed actual data to accurately detect the current profile. This paper describes the implementation details of the application of such techniques by the analysis of two use cases: (1) usage pbkp_rediction of a laser cutter machine; and, (2) occupancy pbkp_rediction in an office environment. We observed that the dataset dimensionality and the cluster intra-variability was greatly reduced, making the profile detection less prone to errors. In conclusion, the implementation of methodologies to enhance the separability of the original data by dimensionality transformations improves the profile discovery and the subsequent actual profile detection.