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Relevant bibliographies by topics / Energy anomaly detection / Dissertations / Theses

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Dissertations / Theses on the topic 'Energy anomaly detection'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Havugimana, Léonce. "IDENTIFYING UNUSUAL ENERGY CONSUMPTIONS OF HOUSEHOLDS : Using Inductive Conformal Anomaly Detection approach." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20220.

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2

Bergentz, Tobias. "Identifying symptoms of fault in District Heating Substations : An investigation in how a predictive heat load software can help with fault detection." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-174442.

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District heating delivers more than 70% of the energy used for heating and domestichot water in Swedish buildings. To stay competitive, district heating needs toreduce its losses and increase capabilities to utilise low grade heat. Finding faultysubstations is one way to allow reductions in supply temperatures in district heatingnetworks, which in turn can help reduce the losses. In this work three suggestedsymptoms of faults: abnormal quantization, drifting and anomalous values, are investigatedwith the help of hourly meter data of: heat load, volume flow, supplyand return temperatures from d

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3

Guss, Herman, and Linus Rustas. "Applying Machine Learning Algorithms for Anomaly Detection in Electricity Data : Improving the Energy Efficiency of Residential Buildings." Thesis, Uppsala universitet, Byggteknik och byggd miljö, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415507.

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The purpose of this thesis is to investigate how data from a residential property owner can be utilized to enable better energy management for their building stock. Specifically, this is done through the development of two machine learning models with the objective of detecting anomalies in the existing data of electricity consumption. The dataset consists of two years of residential electricity consumption for 193 substations belonging to the residential property owner Uppsalahem. The first of the developed models uses the K-means method to cluster substations with similar consumption pattern

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4

Wang, Qinghua. "Traffic analysis, modeling and their applications in energy-constrained wireless sensor networks on network optimization and anomaly detection /." Doctoral thesis, Sundsvall : Tryckeriet Mittuniversitetet, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-10690.

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5

Pol, Adrian Alan. "Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASS083.

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La surveillance de la qualité des données qui proviennent des expériences de physique des hautes énergies est une tâche exigeante mais cruciale pour assurer que les analyses physiques sont basées en données de la meilleure qualité possible. Lors de l’expérience Compact Muon Solenoid opérant au Grand collisionneur de hadrons du CERN, le paradigme actuel d’évaluation de la qualité des données est basé sur l’examen détaillé d’un grand nombre de tests statistiques. Cependant, la complexité toujours croissante des détecteurs et le volume des données de surveillance appellent un changement de paradi

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6

Halaj, Jozef. "Detekce anomálií v IoT sítích." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417286.

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The goal of the thesis was an analysis of IoT communication protocols, their vulnerabilities and the creation of a suitable anomaly detector. It must be possible to run the detector on routers with the OpenWRT system. To create the final solution, it was necessary to analyze the communication protocols BLE and Z-Wave with a focus on their security and vulnerabilities. Furthermore, it was necessary to analyze the possibilities of anomaly detection, design and implement the detection system. The result is a modular detection system based on the NEMEA framework. The detection system is able to de

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7

Dridi, Aicha. "A novel efficient time series deep learning approach using classification, prediction and reinforcement : energy and telecom use case." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS010.

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La croissance massive des capteurs (température, humidité, accéléromètre, capteur de position) et des appareils mobiles (smartphones, tablettes, smartwatch …) fait que la quantité de données générées augmente de manière explosive. Cette immense quantité de données peut être collectée et gérée. Le travail réalisé durant cette thèse vise à proposer en un premier temps une approche qui traite un type de données spécifique qui sont les séries temporelles. Pour ce faire nous avons utilisé des méthodes de classification basées sur des réseaux de neurones convolutifs ainsi que des multi layer percept

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8

Boudargham, Nadine. "Competent QoS-aware and energy efficient protocols for body sensor networks." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCD007.

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Les réseaux de capteurs corporels (RC2) sont constitués de bio-capteurs à faibles ressources énergétiques et de calcul. Ils collectent des données physiologiques du corps humain et de son environnement, et les transmettent à un coordinateur comme le PDA (Personal Digital Assistant) ou un smartphone, pour être acheminé ensuite aux experts de santé. Les réseaux de capteurs corporels collaboratifs (RC3) sont une collection de RC2 qui se déplacent dans une zone donnée et collaborent,interagissent et échangent des données entre eux pour identifier l'activité du groupe et surveiller l'état individue

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9

Martin-Burtart, Nicolas. "Développement d'algorithmes d'analyse spectrale en spectrométrie gamma embarquée." Phd thesis, Université de Strasbourg, 2012. http://tel.archives-ouvertes.fr/tel-00869554.

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Jusqu'au début des années 1980, la spectrométrie gamma aéroportée a avant tout été utilisée pour des applications géophysiques et ne concernait que la mesure des concentrations dans les sols des trois radionucléides naturels (K40, U238 et Th232). Durant les quinze dernières années, un grand nombre de dispositifs de mesures a été développé, la plupart après l'accident de Tchernobyl, pour intervenir en cas d'incidents nucléaires ou de surveillance de l'environnement. Les algorithmes développés ont suivi les différentes missions de ces systèmes. La plupart sont dédiés à l'extraction des signaux à

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10

Cherdo, Yann. "Détection d'anomalie non supervisée sur les séries temporelle à faible coût énergétique utilisant les SNNs." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4018.

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Dans le cadre de la maintenance prédictive du constructeur automobile Renault, cette thèse vise à fournir des solutions à faible coût énergétique pour la détection non supervisée d'anomalies sur des séries temporelles. Avec l'évolution récente de l'automobile, de plus en plus de données sont produites et doivent être traitées par des algorithmes d'apprentissage automatique. Ce traitement peut être effectué dans le cloud ou directement à bord de la voiture. Dans un tel cas, la bande passante du réseau, les coûts des services cloud, la gestion de la confidentialité des données et la perte de don

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11

Branco, Pedro Miguel Mayer. "Anomaly detection in photovoltaic systems." Master's thesis, 2019. http://hdl.handle.net/10362/72312.

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Internship report presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with a specialization in Information Analysis and Management<br>Photovoltaic (PV) solar energy is the fastest-growing renewable source of energy, and poised to become the world’s largest source of electricity by 2050. To maximize efficiency and remain a viable alternative energy source, PV systems should ideally operate seamlessly without anomalies. In reality, however, several kinds of anomalies may occur that prevent PV systems from operating at their full capacity.

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12

Salik, Farakh Nayaab John. "Acoustic anomaly detection using robust statistical energy processing." Thesis, 2007. http://spectrum.library.concordia.ca/975595/1/MR40896.pdf.

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An anomaly is the specific event that causes the violation of a process observer's expectations about the process under observation. In this work, the problem of spatially locating an acoustic anomaly is addressed. Once reduced to a problem in robust statistics, an automated observer is designed to detect when high energy sources are introduced into an acoustic scene. Accounting for potential energy from signal amplitude, and kinetic energy from signal frequency in wavelet-filtered sub-bands, an outlier a robust statistical characterization scheme was developed using the Teager energy operator

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13

Telaga, Abdi Suryadinata, and 特愛諦. "A Warning System for Energy Consumption and Anomaly Detection." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/16913702881182820497.

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博士<br>國立臺灣科技大學<br>營建工程系<br>105<br>Effective feedback is required to reduce building power consumption and carbon emission. Providing information that can draw attention from building manager and tenants is the first step to identify power consumption potential reduction. As anomalous consumption could provide such information, this study proposes an a warning system (WS) that intelligently analyzes anomalous power consumption from large smart meter data sets of a building space and provides real time anomalous consumption visually to different stakeholders. In this study, anomalous consumption

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14

KUMAR, AMIT. "VIDEO BEHAVIOUR PROFILING AND ANOMALY DETECTION." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/13903.

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M.TECH<br>Public security has become a major issue in public places such as subway stations, banks, malls, airports, etc. Recently we have seen that terrorist activities are growing all over the world. To monitor these kinds of activities, there is an increasing demand of automatic video surveillance systems. In a surveillance system, we need to study the behaviour of the environment whether there is any abnormality in the video or not, in real time. Due to this for real time application in surveillance systems, video behaviour profiling has been a topic of great interest in real time. In

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15

Feng, Menghong. "SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays." 2020. https://scholarworks.umass.edu/masters_theses_2/894.

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There has been significant growth in both utility-scale and residential-scale solar installa- tions in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential- scale installations often lack sensing and instrumentation for performance monitoring and fault detection. As a result, faults may go undetected for long periods of time, resulting in generation and revenue losses for the homeowner. In this thesis, we present SunDown, a sensorless approach designed to detect per-panel f

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16

Irene, Karijadi, and 周寶玉. "Anomaly Detection Analysis for Building Energy Consumption Using Data Mining Technique." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/70084491879745354689.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>105<br>In the past decade, building energy management issue has drawn many attentions, which trigger the development of strategies for improving energy efficiency. Along with the rapid development of technology, the operation of energy consumption grows significantly in recent years, and the expensive electricity cost and carbon emission produced are hard to be ignored. Therefore, it is necessary to develop an energy data analysis mechanism to enhance the energy monitoring system. This thesis presents a data analysis framework for analyzing energy consumption data, a

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17

Viet, Nguyen Hoang, and 阮黃越. "An Online Anomaly Energy Consumption Detection Method Using Modified Local Outlier Factor and Boundary Discriminative Rule." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/nagfy4.

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碩士<br>國立中興大學<br>資訊科學與工程學系<br>106<br>Human activities in office buildings consume 39% of the total energy and their wasteful behaviors account for approximately 30%. This wasteful energy consumption, which is often called abnormal consumption. Reducing abnormal energy consumption rate not only saves money, decreases energy usage, but also helps protect the environment. In this work, we used smart meters to collect energy consumption data from office buildings and converted the data into images. Then, we used these images to estimate the current consumption in normal and abnormal manners. Previo

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18

"Signal Processing and Robust Statistics for Fault Detection in Photovoltaic Arrays." Master's thesis, 2012. http://hdl.handle.net/2286/R.I.14796.

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abstract: Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the "smart grid," an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simu

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19

"Energy Analytics for Infrastructure: An Application to Institutional Buildings." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.45586.

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abstract: Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in

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