Short-term load forecasting for smart water and gas grids: A comparative evaluation

Fagiani, Marco; Squartini, Stefano; Bonfigli, Roberto; Piazza, Francesco

doi:10.1109/eeeic.2015.7165339

Cited by 2 publications

(1 citation statement)

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“…To investigate the problem, the authors' focus has been aimed, in the first place, at consumption forecasting for short-and medium-term prediction in both residential and building scenarios [12], thus highlighting the effect of the grid scale on the consumption trends. Thereafter, since leakage detection in large-scale grids has been investigated in the literature, we focused on the detection of leakages occurring in water and natural grids of residential environments [13].…”

Section: Introductionmentioning

confidence: 99%

A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids

et al. 2016

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Abstract:In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS) algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and One-Class Support Vector Machine (OC-SVM), are adopted, under a comparative perspective. Both residential and office building environments are investigated by means of two datasets. One is the Almanac of Minutely Power dataset (AMPds), and it provides water and gas data consumption at 1, 10 and 30 min of time resolution; the other is the Department of International Development (DFID) dataset, and it provides water and gas data consumption at 30 min of time resolution. The achieved performance, computed by means of the Area Under the Curve (AUC), reaches 90% in the office building case study, thus confirming the suitability of the proposed approach for applications in smart water and gas grids.

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Section: Introductionmentioning

confidence: 99%