Multiscale Data Analytics for Residential Active Power Measurements through Time Series Data Mining

Modern measurement and automation equipment for energy systems collect, store, process and communicate ever increasing quantities of raw data which can be used to build data-driven prediction and classification models. Directly using these large and unprocessed data sets can be inefficient especially in imbalanced class problems, where the positive class is sparsely represented in the training examples, such as classification of micro-scale transients. This is due to the longer time required for model training, and due to the increased possibility of obfuscating the useful information behind noisy readings. The matrix profile represents a computationally efficient and general purpose time series data mining technique which is suitable for embedded deployment in future generation smart meters, and in embedded energy gateways. Our analysis concerns the application of this technique on two types of residential electric power measurement data sets: a detached single family house and an apartment, with variable reporting rate and subsequence size parametrisation. Quantitative results support the findings that such approaches serve as practical instrument for measurement time series preprocessing in energy analytics.

I. Introduction

Traditionally, the power distribution grids were passive by design and no real-time monitoring was required for their operation. However, the expansion of distributed energy resources (DER), especially of renewable sources (RES), together with significant changes in the power profiles of the loads, and expected increase of ad-hoc loads such as electric vehicles, are currently requesting for deployment of advanced metering infrastructure at this part of the grid [1]. Furthermore, enhanced situational awareness is needed for flexible and optimal operation of the current active distribution grids which might incorporate several types of actively controlled entities, such as energy communities [2], microgrids, large charging stations on top of the now classical flexible loads and DER [3]. Next generation smart meters (SM) [4] or micro phasor measurement units (PMUs) [5] are among the advanced metering technologies able to provide high resolution measurements needed for plethora of applications in active distribution grids and smart grids [6]. However, transmitting, processing and analysing these measurements in the raw format, in a central location might be costly, impractical as well as raising privacy concerns [2], [7].