A data analytics-based energy information system (EIS) tool to perform meter-level anomaly detection and diagnosis in buildings
| Autor: | Alfonso Capozzoli, Marco Savino Piscitelli, Roberto Chiosa |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Control and Optimization
Association rule learning Energy management Computer science 020209 energy Energy Engineering and Power Technology 02 engineering and technology 010501 environmental sciences computer.software_genre lcsh:Technology 01 natural sciences Energy information systems 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Engineering (miscellaneous) 0105 earth and related environmental sciences Symbolic aggregate approximation Anomaly detection and diagnosis Association rule mining Building energy management Classification tree building energy management energy information systems anomaly detection and diagnosis classification tree symbolic aggregate approximation association rule mining lcsh:T Renewable Energy Sustainability and the Environment Decision tree learning Process (computing) Energy consumption Data analysis Anomaly detection Data mining computer Energy (signal processing) Energy (miscellaneous) |
| Zdroj: | Energies; Volume 14; Issue 1; Pages: 237 Energies, Vol 14, Iss 237, p 237 (2021) |
| Popis: | Recently, the spread of smart metering infrastructures has enabled the easier collection of building-related data. It has been proven that a proper analysis of such data can bring significant benefits for the characterization of building performance and spotting valuable saving opportunities. More and more researchers worldwide are focused on the development of more robust frameworks of analysis capable of extracting from meter-level data useful information to enhance the process of energy management in buildings, for instance, by detecting inefficiencies or anomalous energy behavior during operation. This paper proposes an innovative anomaly detection and diagnosis (ADD) methodology to automatically detect at whole-building meter level anomalous energy consumption and then perform a diagnosis on the sub-loads responsible for anomalous patterns. The process consists of multiple steps combining data analytics techniques. A set of evolutionary classification trees is developed to discover frequent and infrequent aggregated energy patterns, properly transformed through an adaptive symbolic aggregate approximation (aSAX) process. Then a post-mining analysis based on association rule mining (ARM) is performed to discover the main sub-loads which mostly affect the anomaly detected at the whole-building level. The methodology is developed and tested on monitored data of a medium voltage/low voltage (MV/LV) transformation cabin of a university campus. |
| Databáze: | OpenAIRE |
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