A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings

Roberto Chiosa,Marco Savino Piscitelli,Alfonso Capozzoli

doi:10.3390/en14010237

Roberto Chiosa, Marco Savino Piscitelli + Show 1 more

Open Access

https://doi.org/10.3390/en14010237

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Journal: Energies	Publication Date: Jan 5, 2021
Citations: 19	License type: CC BY 4.0

Affiliation: Polytechnic University of Turin

Abstract

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.

Highlights

The building sector is globally recognized as one of the most energy-intensive, and its energy demand continues to increase as a result of a combination of various factors such as extreme climatic events, increased demand for energy services, and in particular those related to air conditioning and quality of the built environment
The great focus on buildings has been encouraged by the introduction of a robust regulatory framework that puts in evidence the importance of a more responsible building energy management
This paper proposed a multiple-step ADD methodology to automatically detect at whole-building meter level anomalous energy consumption and perform a diagnosis on the sub-loads responsible for that anomalous pattern

Summary

Introduction

The building sector is globally recognized as one of the most energy-intensive, and its energy demand continues to increase as a result of a combination of various factors such as extreme climatic events, increased demand for energy services, and in particular those related to air conditioning and quality of the built environment. According to the International Energy Agency (IEA) for the EU member states, buildings are responsible for around 21% of primary energy consumption [1] As a result, this sector is currently among the most strategic ones for reducing global energy demand, improving energy efficiency, and achieving specific decarbonization targets. The great focus on buildings has been encouraged by the introduction of a robust regulatory framework that puts in evidence the importance of a more responsible building energy management In this perspective, the technological advancements that characterized the world of IoT (Internet of Things) and ICT (information and communication technology) has played a fundamental role in determining an everincreasing spread of advanced monitoring and automation infrastructures in buildings, making it is possible to collect a huge amount of data and information related to the real performance in the operation of such complex systems. The technological advancements that characterized the world of IoT (Internet of Things) and ICT (information and communication technology) has played a fundamental role in determining an everincreasing spread of advanced monitoring and automation infrastructures in buildings, making it is possible to collect a huge amount of data and information related to the real performance in the operation of such complex systems. 4.0/).

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