Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring最新文献

Energy Disaggregation for Small and Medium Businesses and their Operational Characteristics 中小企业能源分解及其运营特点

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427854

Abhinav K. Srivastava, Paras Tehria, B. K. Pandey

{"title":"Energy Disaggregation for Small and Medium Businesses and their Operational Characteristics","authors":"Abhinav K. Srivastava, Paras Tehria, B. K. Pandey","doi":"10.1145/3427771.3427854","DOIUrl":"https://doi.org/10.1145/3427771.3427854","url":null,"abstract":"Small and Medium Businesses (SMBs) make up a large chunk of the total energy demand, whereas non-intrusive load disaggregation has been dominated by applications in the residential domain. Existing on-site sensor-based approaches for energy disaggregation of SMBs are not scalable and limit energy efficiency analysis, usage behavior study, and kind-of-site classification, which are essential for energy management and grid operations. In this paper, we propose a highly scalable, non-intrusive load disaggregation model for SMBs, which is independent of on-site sensor data. The energy disaggregation task presented here uses energy data, demographic information, and weather data of SMB's location. The method employs Gaussian Mixture Models, regression models, and DBSCAN to capture appliances' key characteristics. This process can handle usage anomalies, seasonality, and give reliable estimates at the granularity of energy-data sampling rate. Additionally, SMB's working schedule and operational load are estimated using a combination of K-Means and statistical models. Operational load signatures and working schedule and appliance characteristics can further be used for energy management, efficiency analysis, and site classification. The model has been evaluated using anonymized energy data of different types of commercial spaces such as office sites, restaurants, individual shops, and shopping complexes from the USA (North Carolina, South Carolina, Indiana, and Florida) with total monthly consumption ranging from 800 kWh to 120,000 kWh. Results indicate reliable disaggregation of load into the heating, cooling, and baseload categories along with an estimate of operational hours and operational load at the energy-data sampling rate.","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130120283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Non-Intrusive Load Monitoring of Water Heaters Using Low-Resolution Data 使用低分辨率数据的热水器非侵入式负荷监测

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427853

C. Green, S. Garimella

{"title":"Non-Intrusive Load Monitoring of Water Heaters Using Low-Resolution Data","authors":"C. Green, S. Garimella","doi":"10.1145/3427771.3427853","DOIUrl":"https://doi.org/10.1145/3427771.3427853","url":null,"abstract":"Electric water heaters consume approximately 19% of the total energy in residential buildings and may be used as virtual batteries to provide a variety of ancillary services for the electric grid. With the proliferation of advanced metering infrastructure (AMI) smart meters, vast amounts of data have become available for non-intrusive load disaggregation. Disaggregating major appliances such as water heaters using low-resolution AMI data may assist in assessing residential virtual battery potential at a regional level. In this paper, we assess the use of a graph signal processing algorithm for non-intrusive load disaggregation of a hybrid heat pump water heater. Algorithm performance is evaluated for disaggregating the water heater signal in the presence of a variety of other loads and operational modes. Real-world data sampled in the field at a frequency of 1 minute and downsampled for the purpose of this study to 15 minutes were used to simulate typical AMI meter sampling frequency. Results demonstrate that F-measures (the harmonic mean of precision and recall values) > 0.90 are achievable for water heater disaggregation.","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131466438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Solar Disaggregation: State of the Art and Open Challenges 太阳能分解:技术现状和公开挑战

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3429387

Xinlei Chen, Omid Ardakanian

引用次数: 4

Matrix Factorization for High Frequency Non Intrusive Load Monitoring: Definitions and Algorithms 高频非侵入式负荷监测的矩阵分解:定义与算法

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427847

Simon Henriet, Benoit Fuentes, Umut Simsekli, G. Richard

{"title":"Matrix Factorization for High Frequency Non Intrusive Load Monitoring: Definitions and Algorithms","authors":"Simon Henriet, Benoit Fuentes, Umut Simsekli, G. Richard","doi":"10.1145/3427771.3427847","DOIUrl":"https://doi.org/10.1145/3427771.3427847","url":null,"abstract":"Non Intrusive Load Monitoring has been introduced 30 years ago in order to monitor the electric consumption of specific equipments inside a building without the need of installing multiples sensors. During three decades, researchers and industrials have described the NILM problems according to the electric data available, the desired quantity to be monitored and the application it was used for. As a consequence of the multitude of choices, a lot of different formulations can be found in the literature. This diversity makes it difficult for researchers from general domains such as machine learning to tackle the NILM problem. In this paper we aim at defining the NILM problem as a Matrix Factorization task using high frequency measurements and also to review methods to solve this problem. We start by defining the general concepts driving the NILM problem and then show how to cast high frequency NILM into a Matrix Factorization problem. Once casted as a machine learning problem, we will review general purposes algorithms applicable to this problem such as Independent Component Analysis, Sparse Coding or Semi Non-negative Matrix Factorization and specific NILM methods such as BOLT and IVMF.","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122889118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Virtual metering of heat supplied by hydronic perimeter heaters in variable air volume zones 可变风量区域内循环加热器供热的虚拟计量

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3429389

Darwish Darwazeh, J. Duquette, Burak Gunay

{"title":"Virtual metering of heat supplied by hydronic perimeter heaters in variable air volume zones","authors":"Darwish Darwazeh, J. Duquette, Burak Gunay","doi":"10.1145/3427771.3429389","DOIUrl":"https://doi.org/10.1145/3427771.3429389","url":null,"abstract":"Equipment-level virtual metering provides a nonintrusive tool to understand energy flows in commercial buildings and monitor building energy performance. Virtual metering accuracy depends on the modelling approach and its ability to capture the underlying physical processes. This paper examines the performance of three virtual metering approaches to estimate the heat supplied by zonelevel hydronic perimeter heaters: steady-state inverse greybox modelling, transient inverse greybox modelling, and water-side load disaggregation. Models that represent these three approaches are trained using data from a highly instrumented academic building in Ottawa, Canada. Model parameters are identified using the genetic algorithm and used for creating virtual meters that can estimate the heat added by perimeter heaters into building variable air volume zones. The accuracy of these virtual meters is assessed by comparing them to physical meters installed in these zones. The results indicate that the three modelling approaches can estimate the daily heating energy supplied by perimeter heaters at a normalized RMSE between 13% and 23%.","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117252663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

On the Impact of the Sequence Length on Sequence-to-Sequence and Sequence-to-Point Learning for NILM 序列长度对NILM中序列到序列和序列到点学习的影响

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427857

A. Reinhardt, Mazen Bouchur

{"title":"On the Impact of the Sequence Length on Sequence-to-Sequence and Sequence-to-Point Learning for NILM","authors":"A. Reinhardt, Mazen Bouchur","doi":"10.1145/3427771.3427857","DOIUrl":"https://doi.org/10.1145/3427771.3427857","url":null,"abstract":"The Sequence-to-Sequence (S2S) and Sequence-to-Point (S2P) optimization methods achieve remarkable accuracy results for load disaggregation tasks. Internally, they rely on neural networks, trained to identify the power consumption of a single appliance under consideration from a sequence of aggregate power data. Their most important configuration parameter - the number of input data samples to consider - is, however, mostly set to a fixed value. As a result thereof, the amount of historical data available at the algorithm's input is governed by the sampling interval of the used input data. For example, UK-DALE [5] provides samples every 6 s, so a sequence length of 599 samples (as proposed in [9]) makes approximately 1 h of historical data available to the disaggregation algorithm. No analyses of the impact of the sequence length on the NILM performance have been documented in literature to date. We hence present a methodological assessment of the sensitivity of S2S and S2P to variations of their input sequence length parameter. Our results show that setting a per-device parameter value leads to improved disaggregation results; however, the required values need to be determined empirically, as they are unrelated to the appliances' operational durations. Even if only a single value may be set, an informed choice (rather than using the default value) can drastically improve NILM performance.","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"17 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121004689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

UNet-NILM: A Deep Neural Network for Multi-tasks Appliances State Detection and Power Estimation in NILM UNet-NILM:用于多任务家电状态检测和NILM中功率估计的深度神经网络

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427859

A. Faustine, Lucas Pereira, Hafsa Bousbiat, Shridhar Kulkarni

引用次数: 49

Performance Analysis of Similar Appliances Identification using NILM Technique under Different Data Sampling Rates 不同采样率下基于NILM技术的同类电器识别性能分析

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427858

R. Gopinath, Mukesh Kumar, K. Lokesh, K. Srinivas

{"title":"Performance Analysis of Similar Appliances Identification using NILM Technique under Different Data Sampling Rates","authors":"R. Gopinath, Mukesh Kumar, K. Lokesh, K. Srinivas","doi":"10.1145/3427771.3427858","DOIUrl":"https://doi.org/10.1145/3427771.3427858","url":null,"abstract":"Similar power/similar loads identification using a non-intrusive load monitoring (NILM) system is a challenging task due to overlapping characteristics of similar appliances. Therefore, we need to identify robust features that are capable of distinguishing the events of similar appliances effectively. Further, the developed NILM system/methodology need to be examined for different sampling rates, since the performance evaluation of the algorithm with single sampling rate alone is not sufficient. In this work, we used four compact fluorescent lamps (CFL) which are having same specifications configured in different electrical network combinations. We explored locality constrained linear coding (LLC) based deep neural networks (DNN) to express the input feature vectors in terms of load independent basis vectors in a higher dimensional feature space to make the features robust for effective identification of similar loads in a NILM system. Further, we examined the effectiveness of LLC-DNN approach for the different sampling rates, 10 kHz, 25 kHz and 50 kHz respectively. From our study, it is observed that the performance of the LLC-DNN has been consistent and improves the similar appliances identification accuracy significantly with the increase in sampling rates.","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"106-108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121193524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Finite Precision Analysis for an FPGA-based NILM Event-Detector 基于fpga的NILM事件检测器的有限精度分析

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427849

Rubén Nieto, Laura de Diego-Otón, Álvaro Hernández, J. Ureña

{"title":"Finite Precision Analysis for an FPGA-based NILM Event-Detector","authors":"Rubén Nieto, Laura de Diego-Otón, Álvaro Hernández, J. Ureña","doi":"10.1145/3427771.3427849","DOIUrl":"https://doi.org/10.1145/3427771.3427849","url":null,"abstract":"Most Non-Intrusive Load Monitoring (NILM) techniques often require a correct detection of the events that occur in the mains, in order to carry out a correct identification of the corresponding appliances. For that performance, event-detectors are normally based on signals, such as voltages and currents, acquired by a smart meter at the entrance of the household. In this work, a finite precision analysis is performed for an event detector implemented on a System-on-Chip (SoC) based on a Field-Programmable Gate Array (FPGA) for NILM applications. The proposal employs an integrated circuit (ADE9153A) to perform the signal acquisition at 4 ksamples/s, higher than those used in common smart meters installed at homes nowadays. The purpose of the finite precision analysis is to reduce the hardware resource consumption of the FPGA, while the precision of the event detector is maintained. To validate the design, a comparison is carried out in terms of accuracy and specificity against previous works, as well as a comparison of the event detector using finite precision (fixed-point) and single-precision floating-point resolution. Finally, the hardware resource consumption obtained for the proposed architecture is discussed","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123590054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Lightweight Non-Intrusive Load Monitoring Employing Pruned Sequence-to-Point Learning 采用剪枝序列到点学习的轻量级非侵入式负载监控

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427845

Jack Barber, H. Cuayáhuitl, Mingjun Zhong, Wenpeng Luan

引用次数: 17