Article
Chemistry, Analytical
Muzzamil Ghaffar, Shakil R. Sheikh, Noman Naseer, Zia Mohy Ud Din, Hafiz Zia Ur Rehman, Muhammad Naved
Summary: This research proposes two novel non-intrusive load monitoring techniques using spectral clustering to extract individual appliance energy usage from the aggregate energy profile of a building. The performance evaluation shows that these techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time.
Article
Energy & Fuels
Soumyajit Ghosh, Debashis Chatterjee
Summary: Smart meter technology is crucial in the context of smart grid connected residential load system, with non-intrusive load monitoring being a well-known method to assess power consumption and operating behavior of individual loads. The issue of identifying harmonic polluting loads has arisen due to modern household appliances injecting unwanted harmonics into the system. An improved technique using input aggregated voltage-current data and an Artificial Bee Colony algorithm has been proposed for load monitoring without heavy training mechanisms, showing promising results in comparison to state-of-the-art techniques.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2021)
Article
Chemistry, Analytical
Nasrin Kianpoor, Bjarte Hoff, Trond Ostrem
Summary: This study proposes an adaptive ensemble filtering framework integrated with long short-term memory (LSTM) for identifying flexible loads. The framework, called AEFLSTM, combines filtering techniques and LSTM to filter noise and learn the long-term dependencies of flexible loads. It also searches ensemble filtering techniques to find the best method for disaggregating different loads.
Article
Energy & Fuels
Krzysztof Dowalla, Piotr Bilski, Robert Lukaszewski, Augustyn Wojcik, Ryszard Kowalik
Summary: The paper presents a novel non-intrusive method for identifying appliances by analyzing changes in the common supply current signal. The method achieves high accuracy by processing the signal in the time domain, preserving the information in the original signal. Experimental results demonstrate the effectiveness of the approach in real-world scenarios.
Review
Engineering, Electrical & Electronic
Pascal A. Schirmer, Iosif Mporas
Summary: The rapid development of technology in the electrical energy sector has led to increased electric power needs. This has resulted in the adoption of smart-meters and smart-grids, as well as the rise of Load Monitoring (LM) using Non-Intrusive Load Monitoring (NILM) for appliance-specific energy monitoring. The article provides a review of NILM approaches, groups previously published results, and includes a software implementation of the described NILM approaches.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Energy & Fuels
Xiaochao Guo, Chao Wang, Tao Wu, Ruiheng Li, Houyi Zhu, Huaiqing Zhang
Summary: Results from Non-Intrusive Load Monitoring can be used for energy decomposition and load identification, facilitating effective energy consumption management. However, existing studies often focus on fixed settings, which limits the practical application in real-world scenarios. In this study, we propose a novel appliance detection method that analyzes pattern variations of aggregated power sequences and can determine new appliances without prior information, establishing a basis for load monitoring in scenarios with dynamic changes in load topology. Experimental results show significant improvement in F1 metric compared to event-based approaches.
Article
Engineering, Electrical & Electronic
Dong Hua, Fanqi Huang, Longjun Wang, Wutao Chen
Summary: This paper studies the event-based NILM approach using a mixed linear integer programming (MILP) model, which can disaggregate power consumption of multiple appliances switched simultaneously. By utilizing non-dominated sorting genetic algorithm II and cumulative sum (CUSUM) technique, a tradeoff between precision and recall can be achieved.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Review
Chemistry, Analytical
Maria Kaselimi, Eftychios Protopapadakis, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis
Summary: This paper provides a comprehensive review of the development of non-intrusive load monitoring (NILM), focusing on the transition from mathematical modeling to practical application. The paper reviews and analyzes recent research on NILM methods for residential appliances, highlighting the key findings and discussing the research dilemmas in applying NILM methods. The paper emphasizes the need to transform traditional disaggregation models into a practical and trustworthy framework.
Article
Construction & Building Technology
Alan Meier, Dan Cautley
Summary: Non-intrusive load monitoring system (NILM) in commercial buildings faces challenges in load disaggregation due to the number and complexity of loads, difficulty in interpreting small changes in power consumption, and inability to identify continuously operating loads. Obtaining data sets for evaluation of NILM technologies in actual buildings is hindered by disruptions to occupants, misidentification errors, measurement errors, and expense. Enhancements to basic NILM approaches include tagging key devices, hybrid or supplemental metering, and applying insights from engineering knowledge and audits.
ENERGY AND BUILDINGS
(2021)
Review
Engineering, Electrical & Electronic
Hafiz Khurram Iqbal, Farhan Hassan Malik, Aoun Muhammad, Muhammad Ali Qureshi, Muhammad Nawaz Abbasi, Abdul Rehman Chishti
Summary: NILM is a hot topic among researchers, with energy disaggregation datasets used as benchmarks for algorithm validation. This paper provides a comprehensive review of 42 NILM datasets, highlighting strengths, limitations, and future research directions.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Energy & Fuels
Tamara Todic, Vladimir Stankovic, Lina Stankovic
Summary: With the widespread deployment of smart meters, Non-Intrusive Load Monitoring (NILM) has emerged as a promising application for informing energy management within buildings. However, existing deep learning NILM models have limitations in terms of flexibility and scalability. In this study, an active learning framework is proposed to improve transferability and reduce the cost of labeling, achieving optimal accuracy-labelling effort trade-off. The results demonstrate the potential of this approach in improving the performance of NILM models and reducing computational resources needed.
Article
Engineering, Electrical & Electronic
Chao Wang, Zhao Wu, Wenxiong Peng, Weihua Liu, Linyun Xiong, Tao Wu, Lili Yu, Huaiqing Zhang
Summary: NILM is an energy-saving technology that estimates energy consumption information from aggregated power data. This paper presents an Adaptive Factorial Hidden Markov Model (Adaptive-FHMM) to characterize appliance working states transition and predict power consumption. Experimental results demonstrate that the proposed model outperforms state-of-the-art models in various metrics.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Energy & Fuels
Yuanshi Zhang, Wenyan Qian, Yujian Ye, Yang Li, Yi Tang, Yu Long, Meimei Duan
Summary: The increasing effects of global warming and energy depletion have led to concerns about the pollution caused by traditional oil and fossil energy usage. Distributed energy resources (DERs) are seen as a promising solution to address these issues. However, the growing proportion of power injection from DERs presents technical challenges that could destabilize the grid. Non intrusive load monitoring (NILM) is a cost-effective approach that can provide residential power information to improve grid scheduling and optimize power consumption behavior.
Article
Computer Science, Information Systems
Muzzamil Ghaffar, Shakil Rehman Sheikh, Noman Naseer, Syed Ali Usama, Bashir Salah, Soliman Abdul Karim Alkhatib
Summary: The widespread use of smart meter data in modern grids is driving stakeholders to utilize it for demand response management and achieving energy sustainability goals. Non-Intrusive Load Monitoring (NILM) is being used as a method to disaggregate individual devices from a combined load profile. This study combines two spectral clustering strategies using voting-based consensus clustering technique to achieve the benefits of both strategies and achieves enhanced overall performance.
Article
Construction & Building Technology
Zhao Wu, Chao Wang, Wenxiong Peng, Weihua Liu, Huaiqing Zhang
Summary: This paper introduces an Adaptive Density Peak Clustering-Factorial Hidden Markov Model (ADPC-FHMM) that can automatically determine the working states of appliances with reduced dependency on prior information. Case studies show that the proposed model outperforms its counterparts on metrics such as Accuracy, F-measure, and MAE.
ENERGY AND BUILDINGS
(2021)
Article
Energy & Fuels
Dandan Li, Jiangfeng Li, Xin Zeng, Vladimir Stankovic, Lina Stankovic, Changjiang Xiao, Qingjiang Shi
Summary: This study explores the importance of reducing carbon emissions in buildings to overall greenhouse gas emissions reductions and proposes a method for estimating appliance power consumption based on transfer learning. By utilizing a one-to-many model and incorporating appliance transfer learning and cross-domain transfer learning, this approach effectively estimates the power consumption of all appliances.
Article
Chemistry, Analytical
Jiangfeng Li, Lina Stankovic, Vladimir Stankovic, Stella Pytharouli, Cheng Yang, Qingjiang Shi
Summary: This paper proposes a novel multi-channel event-detection scheme based on Neyman-Pearson lemma and Multi-channel Coherency Migration (MCM) on the stacked signal across multi-channels to improve the classification of seismic signals. It also uses graph-based feature weight optimization as feature selection to enhance signal classification. Experimental results show that this method can identify 614 more seismic events compared to traditional detection approaches, and feature selection provides more focused feature sets while improving the classification performance.
Review
Chemistry, Analytical
Dong Han, Beni Mulyana, Vladimir Stankovic, Samuel Cheng
Summary: This review provides an overview of recent advances in deep reinforcement learning algorithms for robotic manipulation tasks. It covers the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. Various deep reinforcement learning algorithms, including value-based methods, policy-based methods, and actor-critic approaches, are discussed. The review also examines the challenges and solutions when applying these algorithms to robotics tasks, and highlights unsolved research issues and future directions for the subject.
Article
Chemistry, Analytical
Rachel Stephen Mollel, Lina Stankovic, Vladimir Stankovic
Summary: With the global roll-out of smart metering, the potential of higher resolution energy readings is being tapped into for accurate billing, improved demand response, and better-tuned tariffs. This paper focuses on the trustworthiness of the NILM model and proposes a naturally interpretable decision tree approach, as well as explainability tools, to improve appliance classification performance and feature selection. Experimental results show significant improvements in the classification performance of appliances like the toaster, dishwasher, and washing machine.
Article
Energy & Fuels
Wenpeng Luan, Longfei Tian, Bochao Zhao
Summary: Dynamic tariffs are essential in demand response as they help smooth power consumption, reduce generation capacity requirement, and carbon emissions. However, existing works often overlook important factors such as user responses to tariffs when designing them. To address this issue, this paper proposes a new dynamic tariff design method that considers user responses to tariff changes. The method utilizes non-intrusive load monitoring technique to acquire information on rated power and user preferences for each appliance, which is then used to quantify user comfort or discomfort based on their appliance usage habits. A bi-level Stackelberg game model is then built to design optimal dynamic tariffs and simulate the impact of tariff changes on users' demand response plans. The results show that the proposed model generally outperforms benchmark methods in achieving peak shaving, low carbon emission, and user satisfaction.
Article
Energy & Fuels
Tamara Todic, Vladimir Stankovic, Lina Stankovic
Summary: With the widespread deployment of smart meters, Non-Intrusive Load Monitoring (NILM) has emerged as a promising application for informing energy management within buildings. However, existing deep learning NILM models have limitations in terms of flexibility and scalability. In this study, an active learning framework is proposed to improve transferability and reduce the cost of labeling, achieving optimal accuracy-labelling effort trade-off. The results demonstrate the potential of this approach in improving the performance of NILM models and reducing computational resources needed.
Review
Energy & Fuels
Cheng Yang, Yupeng Sun, Yujie Zou, Fei Zheng, Shuangyu Liu, Bochao Zhao, Ming Wu, Haoyang Cui
Summary: This study aims to comprehensively understand the impact of distributed generators (DGs) in distribution networks (DNs) on optimal power flow (OPF) and propose potential solutions. By delving into the problem formulation and different optimization techniques, it becomes easier to select appropriate solutions for real-world OPF problems. Furthermore, the paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of OPF.
Article
Geochemistry & Geophysics
Jiangfeng Li, Minxiang Ye, Lina Stankovic, Vladimir Stankovic, Stella Pytharouli
Summary: This study proposes a multitask learning scheme that utilizes physical characteristics of seismic wave propagation and a 3-D P-wave velocity model for signal representation learning. The results show that this approach outperforms state-of-the-art methods.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Geochemistry & Geophysics
Jiaxin Jiang, Vladimir Stankovic, Lina Stankovic, Emmanouil Parastatidis, Stella Pytharouli
Summary: Passive seismics are used to understand underground processes and predict their effects. Manual detection of seismic events is time-consuming and prone to inconsistency, so an automated approach based on convolutional neural networks (CNN) is proposed. Three different CNN architectures are evaluated using continuous seismometer recordings from a landslide area, showing excellent performance. The proposed network can also detect earthquake events in a different seismic area, demonstrating its potential to replace manual labeling.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Shuyi Chen, Bochao Zhao, Mingjun Zhong, Wenpeng Luan, Yixin Yu
Summary: This article proposes a self-supervised learning approach that allows training deep learning models for nonintrusive load monitoring with limited labeled data. This addresses the challenge of generalizing trained models to different sites with varying load characteristics and appliance operating patterns.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Giulia Tanoni, Lina Stankovic, Vladimir Stankovic, Stefano Squartini, Emanuele Principi
Summary: This article proposes a knowledge distillation approach for NILM, aiming to reduce model complexity and improve generalization on unseen data domains. Experimental results show that the approach outperforms benchmark methods in multilabel appliance classification.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
