Abstract
Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this article, we propose a novel change detection method, called ECHAD (Embedding-based CHAnge Detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with state-of-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections.
Highlights
P OWER grids are complex systems consisting of generation, transmission, and distribution infrastructures
Approaches based on one-class learning [2]–[6] find their natural application, since the built model is fitted on one scenario and can subsequently be exploited to detect changes. Following this line of research, in this paper we propose ECHAD (Embedding-based CHAnge Detection), a novel unsupervised change detection method able to analyze streaming data generated by sensors located in smart grids
THE METHOD ECHAD we describe our novel approach ECHAD, an embedding-based change detection algorithm that is able to detect changes in time series data generated by smart grids
Summary
P OWER grids are complex systems consisting of generation, transmission, and distribution infrastructures They represent an important evolution of power grids, where clients are not necessarily passive consumers but have the opportunity to actively participating in the grid, by producing energy from renewable sources and by storing energy through batteries or alternative systems. The production of energy from renewable sources is inherently characterized by instability issues due, for example, to weather conditions This uncertainty may negatively impact the performance of analytical tools used in power grids for scheduling, planning and regulation purposes. Significant efforts have been devoted to the forecasting of the energy produced by plants in smart grids [7]–[11] Solving this task is important to support grid power balancing, especially when the energy is produced by renewable sources. In [17], the authors exploit geodistributed weather observations in the neighborhood of wind plants, while in [14], the authors extract statistical indicators that model the spatio-temporal autocorrelation between plants for each descriptive feature
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