Dominik Egarter | Alpen-Adria-Universität Klagenfurt (original) (raw)

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Papers by Dominik Egarter

Research paper thumbnail of PALDi: Online Load Disaggregation via Particle Filtering

Smart metering and fine-grained energy data are one of the major enablers for the future smart gr... more Smart metering and fine-grained energy data are one of the major enablers for the future smart grid and improved energy efficiency in smart homes. By using the information provided by smart meter power draw, valuable information can be extracted as disaggregated appliance power draws by non-intrusive load monitoring (NILM). NILM allows to identify appliances according to their power characteristics in the total power consumption of a household, measured by one sensor, the smart meter. In this paper we present a NILM approach, where the appliance states are estimated by particle filtering (PF). PF is used for non-linear and non-Gaussian disturbed problems and is suitable to estimate the appliance state. On/off appliances, multi-state appliances, or combinations of them are modeled by hidden Markov models (HMM) and their combinations result in a factorial hidden Markov model (FHMM) modeling the household power demand. We evaluate the PF-based NILM approach on synthetic and on real data from a well-known dataset to show that our approach achieves an accuracy of 90% on real household power draws.

Research paper thumbnail of Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy cons... more The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS.

Research paper thumbnail of Load Hiding of Household's Power Demand

With the development and introduction of smart metering, the energy information for costumers wil... more With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art batterybased load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy.

Research paper thumbnail of GREEND: An Energy Consumption Dataset of Households in Italy and Austria

Home energy management systems can be used to monitor and optimize consumption and local producti... more Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we benchmark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining.

Research paper thumbnail of Appliance State Estimation based on Particle Filtering

Non-Intrusive Load Monitoring is a single-point metering approach to identify and to monitor hous... more Non-Intrusive Load Monitoring is a single-point metering approach to identify and to monitor household appliances according their appliance power characteristics. In this paper, we propose an unsupervised classification approach for appliance state estimation of on/off-appliances modeled by a Hidden Markov Model (HMM). To estimate the states of appliances, we use the sequential Monte Carlo or particle filtering (PF) method. The proposed algorithm is tested with MATLAB simulations and is evaluated according to correctly or incorrectly detected on/off events.

Research paper thumbnail of Integrating Households into the Smart Grid

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The success of the Smart Grid depends on its ability to collect data from heterogeneous sources s... more The success of the Smart Grid depends on its ability to collect data from heterogeneous sources such as smart meters and smart appliances, as well as the utilization of this information to forecast energy demand and to provide value-added services to users. In our analysis, we discuss requirements for collecting and integrating household data within smart grid applications. We put forward a potential system architecture and report stateof-the-art technologies that can be deployed towards this vision.

Research paper thumbnail of EvoNILM -Evolutionary Appliance Detection for Miscellaneous Household Appliances

To improve the energy awareness of consumers, it is necessary to provide them with information ab... more To improve the energy awareness of consumers, it is necessary to provide them with information about their energy demand, not just on the household level. Non-intrusive load monitoring (NILM) gives the consumer the opportunity to disaggregate their consumed power on the appliance level. The consumer is provided with information about the energy demand of each individual appliances. In this paper we present an evolutionary optimization algorithm, applicable to NILM purposes. It can be used to detect appliances with a probabilistic power demand model. We show that the detection performance of the evolutionary algorithm can be improved if the single population approach of the evolutionary algorithm is replaced by a parallel population approach with individual exchange and by the introduction of application-oriented preprocessing and mutation methods. The proposed algorithm is tested with Matlab simulations and is evaluated according to the fitness reached and detection probability of the algorithm.

Research paper thumbnail of Evolving Non-Intrusive Load Monitoring

Applications of Evolutionary …, 2013

Non-intrusive load monitoring (NILM) identifies used appliances in a total power load according t... more Non-intrusive load monitoring (NILM) identifies used appliances in a total power load according to their individual load characteristics. In this paper we propose an evolutionary optimization algorithm to identify appliances, which are modeled as on/off appliances. We evaluate our proposed evolutionary optimization by simulation with Matlab, where we use a random total load and randomly generated power profiles to make a statement of the applicability of the evolutionary algorithm as optimization technique for NILM. Our results shows that the evolutionary approach is feasible to be used in NILM systems and can reach satisfying detection probabilities.

Research paper thumbnail of Design guidelines for smart appliances

Intelligent Solutions in Embedded …, 2012

Embedded intelligence can help controlling and reducing the energy consumption of appliances to a... more Embedded intelligence can help controlling and reducing the energy consumption of appliances to a significant amount. Such a smart appliance will consist of a communication interface, a local processing and decision unit and the appliance's actual function. Sophisticated functions for such a device will involve a notion of real-time with a respective time format, a generic database that contains energy usage logs, error messages, warnings and real-time measurements for power usage, and an embedded self-description that allows to integrate the device into a system with minimum manual configuration. While there exists concepts for smart plugs and smart outlets that can be applied to "smarten" an existing device, in general we need to assume that the variety of appliances and technologies will require the support for various architectures including software solutions that integrate into the functions of an appliance with existing computing power, e.g. a DVD player or a state-of-theart television set. Thus there is a need for architectural services with flexibility for different hosting systems while keeping the interoperability with respect to a smart home control system.

Research paper thumbnail of PALDi: Online Load Disaggregation via Particle Filtering

Smart metering and fine-grained energy data are one of the major enablers for the future smart gr... more Smart metering and fine-grained energy data are one of the major enablers for the future smart grid and improved energy efficiency in smart homes. By using the information provided by smart meter power draw, valuable information can be extracted as disaggregated appliance power draws by non-intrusive load monitoring (NILM). NILM allows to identify appliances according to their power characteristics in the total power consumption of a household, measured by one sensor, the smart meter. In this paper we present a NILM approach, where the appliance states are estimated by particle filtering (PF). PF is used for non-linear and non-Gaussian disturbed problems and is suitable to estimate the appliance state. On/off appliances, multi-state appliances, or combinations of them are modeled by hidden Markov models (HMM) and their combinations result in a factorial hidden Markov model (FHMM) modeling the household power demand. We evaluate the PF-based NILM approach on synthetic and on real data from a well-known dataset to show that our approach achieves an accuracy of 90% on real household power draws.

Research paper thumbnail of Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy cons... more The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS.

Research paper thumbnail of Load Hiding of Household's Power Demand

With the development and introduction of smart metering, the energy information for costumers wil... more With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art batterybased load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy.

Research paper thumbnail of GREEND: An Energy Consumption Dataset of Households in Italy and Austria

Home energy management systems can be used to monitor and optimize consumption and local producti... more Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we benchmark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining.

Research paper thumbnail of Appliance State Estimation based on Particle Filtering

Non-Intrusive Load Monitoring is a single-point metering approach to identify and to monitor hous... more Non-Intrusive Load Monitoring is a single-point metering approach to identify and to monitor household appliances according their appliance power characteristics. In this paper, we propose an unsupervised classification approach for appliance state estimation of on/off-appliances modeled by a Hidden Markov Model (HMM). To estimate the states of appliances, we use the sequential Monte Carlo or particle filtering (PF) method. The proposed algorithm is tested with MATLAB simulations and is evaluated according to correctly or incorrectly detected on/off events.

Research paper thumbnail of Integrating Households into the Smart Grid

mobile.aau.at

The success of the Smart Grid depends on its ability to collect data from heterogeneous sources s... more The success of the Smart Grid depends on its ability to collect data from heterogeneous sources such as smart meters and smart appliances, as well as the utilization of this information to forecast energy demand and to provide value-added services to users. In our analysis, we discuss requirements for collecting and integrating household data within smart grid applications. We put forward a potential system architecture and report stateof-the-art technologies that can be deployed towards this vision.

Research paper thumbnail of EvoNILM -Evolutionary Appliance Detection for Miscellaneous Household Appliances

To improve the energy awareness of consumers, it is necessary to provide them with information ab... more To improve the energy awareness of consumers, it is necessary to provide them with information about their energy demand, not just on the household level. Non-intrusive load monitoring (NILM) gives the consumer the opportunity to disaggregate their consumed power on the appliance level. The consumer is provided with information about the energy demand of each individual appliances. In this paper we present an evolutionary optimization algorithm, applicable to NILM purposes. It can be used to detect appliances with a probabilistic power demand model. We show that the detection performance of the evolutionary algorithm can be improved if the single population approach of the evolutionary algorithm is replaced by a parallel population approach with individual exchange and by the introduction of application-oriented preprocessing and mutation methods. The proposed algorithm is tested with Matlab simulations and is evaluated according to the fitness reached and detection probability of the algorithm.

Research paper thumbnail of Evolving Non-Intrusive Load Monitoring

Applications of Evolutionary …, 2013

Non-intrusive load monitoring (NILM) identifies used appliances in a total power load according t... more Non-intrusive load monitoring (NILM) identifies used appliances in a total power load according to their individual load characteristics. In this paper we propose an evolutionary optimization algorithm to identify appliances, which are modeled as on/off appliances. We evaluate our proposed evolutionary optimization by simulation with Matlab, where we use a random total load and randomly generated power profiles to make a statement of the applicability of the evolutionary algorithm as optimization technique for NILM. Our results shows that the evolutionary approach is feasible to be used in NILM systems and can reach satisfying detection probabilities.

Research paper thumbnail of Design guidelines for smart appliances

Intelligent Solutions in Embedded …, 2012

Embedded intelligence can help controlling and reducing the energy consumption of appliances to a... more Embedded intelligence can help controlling and reducing the energy consumption of appliances to a significant amount. Such a smart appliance will consist of a communication interface, a local processing and decision unit and the appliance's actual function. Sophisticated functions for such a device will involve a notion of real-time with a respective time format, a generic database that contains energy usage logs, error messages, warnings and real-time measurements for power usage, and an embedded self-description that allows to integrate the device into a system with minimum manual configuration. While there exists concepts for smart plugs and smart outlets that can be applied to "smarten" an existing device, in general we need to assume that the variety of appliances and technologies will require the support for various architectures including software solutions that integrate into the functions of an appliance with existing computing power, e.g. a DVD player or a state-of-theart television set. Thus there is a need for architectural services with flexibility for different hosting systems while keeping the interoperability with respect to a smart home control system.