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S. Drenker

Bio: S. Drenker is an academic researcher from Electric Power Research Institute. The author has contributed to research in topics: Nonintrusive load monitoring & Energy consumption. The author has an hindex of 1, co-authored 1 publications receiving 205 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors describe how the Electric Power Research Institute (EPRI) commissioned an implementation and commercialization of a nonintrusive appliance load monitoring system (NIALMS) based on EPRI-developed intellectual property.
Abstract: The authors describe how the Electric Power Research Institute (EPRI) commissioned an implementation and commercialization of a nonintrusive appliance load monitoring system (NIALMS) based on EPRI-developed intellectual property. The system determines the energy consumption of individual appliances being turned on and off within a whole building's electric load.

213 citations


Cited by
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Journal ArticleDOI
06 Dec 2012-Sensors
TL;DR: This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing, review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.
Abstract: Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

850 citations

Journal ArticleDOI
TL;DR: In this article, the authors present techniques for high-performance non-intrusive load and diagnostic monitoring and illustrate key points with results from field tests, as well as demonstrate the performance of these techniques.
Abstract: Nonintrusive load monitoring (NILM) can determine operating schedule of electrical loads in a target system from measurements made at a centralized location, such as the electric utility service entry. NILM is an ideal platform for extracting useful information about any system that uses electromechanical devices. It has a low installation cost and high reliability because it uses a bare minimum of sensors. It is possible to use modem state and parameter estimation algorithms to verify remotely the "health" of electromechanical loads by using NILM to analyze measured waveforms associated with the operation of individual loads. NILM can also monitor the operation of the electrical distribution system itself, identifying situations where two or more otherwise healthy loads interfere with each other's operation through voltage waveform distortion or power quality problems. Strategies for nonintrusive monitoring have developed over the last 20 years. Advances in computing technology make a new wealth of computational tools useful in practical, field-based NILM systems. This article reviews techniques for high-performance nonintrusive load and diagnostic monitoring and illustrates key points with results from field tests.

607 citations

Journal ArticleDOI
TL;DR: In this article, a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or whole-building, energy signal is presented. And the authors explain how appliance-level data affords numerous benefits and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data.

549 citations

Journal ArticleDOI
TL;DR: The basic concept, features of load signatures, structure and methodology of applying mathematical programming techniques, pattern recognition tools, and committee decision mechanism to perform load disaggregation are depicted.
Abstract: Load signature is the unique consumption pattern intrinsic to each individual electrical appliance/piece of equipment. This paper focus on building a universal platform to better understand and explore the nature of electricity consumption patterns using load signatures and advanced technology, such as feature extraction and intelligent computing. Through this knowledge, we can explore and develop innovative applications to achieve better utilization of resources and develop more intelligent ways of operation. This paper depicts the basic concept, features of load signatures, structure and methodology of applying mathematical programming techniques, pattern recognition tools, and committee decision mechanism to perform load disaggregation. New indices are also introduced to aid our understanding of the nature of load signatures and different disaggregation algorithms.

489 citations

Journal ArticleDOI
TL;DR: In this article, the authors examined the data requirements for some of the proposed applications of smart meter data within the electricity supply industry, and investigated whether the use of personal data can be minimized or even avoided.

328 citations