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A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem
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An up to date overview of NILM system and its associated methods and techniques for energy disaggregation problem is presented and the review of the state-of-the art NILm algorithms are reviewed.Abstract:
The rapid urbanization of developing countries coupled with explosion in construction of high rising buildings and the high power usage in them calls for conservation and efficient energy program. Such a program require monitoring of end-use appliances energy consumption in real-time. The worldwide recent adoption of smart-meter in smart-grid, has led to the rise of Non-Intrusive Load Monitoring (NILM); which enables estimation of appliance-specific power consumption from building's aggregate power consumption reading. NILM provides households with cost-effective real-time monitoring of end-use appliances to help them understand their consumption pattern and become part and parcel of energy conservation strategy. This paper presents an up to date overview of NILM system and its associated methods and techniques for energy disaggregation problem. This is followed by the review of the state-of-the art NILM algorithms. Furthermore, we review several performance metrics used by NILM researcher to evaluate NILM algorithms and discuss existing benchmarking framework for direct comparison of the state of the art NILM algorithms. Finally, the paper discuss potential NILM use-cases, presents an overview of the public available dataset and highlight challenges and future research directions.read more
Citations
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Journal ArticleDOI
Energy management using non-intrusive load monitoring techniques - State-of-the-art and future research directions
TL;DR: This paper presents the comprehensive review of state-of-the-art algorithms that have been explored by the researchers towards developing an accurate NILM system for effective energy management and potential applications of NilM in different domains and its future research directions are discussed.
Proceedings ArticleDOI
Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring
Maria Kaselimi,Nikolaos Doulamis,Anastasios Doulamis,Athanasios Voulodimos,Eftychios Protopapadakis +4 more
TL;DR: A Bayesian-optimized bidirectional Long Short -Term Memory (LSTM) method for energy disaggregation, which is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increase.
Journal ArticleDOI
Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models
Maria Kaselimi,Nikolaos Doulamis,Athanasios Voulodimos,Eftychios Protopapadakis,Anastasios Doulamis +4 more
TL;DR: A non-causal adaptive context-aware bidirectional deep learning model for energy disaggregation that harnesses the representational power of deep recurrent Long Short-Term Memory neural networks, while fitting two basic properties of NILM problem which state of the art methods do not appropriately account for.
Journal ArticleDOI
A Cloud-Based On-Line Disaggregation Algorithm for Home Appliance Loads
Million Abayneh Mengistu,Awet Abraha Girmay,Christian Camarda,Andrea Acquaviva,Edoardo Patti +4 more
TL;DR: An on-line-non-intrusive load monitoring machine learning algorithm combining two methodologies: 1) unsupervised event-based profiling and 2) Markov chain appliance load modeling is proposed.
Journal ArticleDOI
Can non-intrusive load monitoring be used for identifying an appliance's anomalous behaviour?
TL;DR: This paper proposes an anomaly detection algorithm which performs well for submetering data and evaluates its ability to identify the same faulty behaviour of appliances but with NILM-generated appliance power traces, and shows that NilM traces are not as robust to identification of faulty behaviour as compared to using submetered data.
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