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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 23/2020

02.05.2020 | S.I.: Emerging applications of Deep Learning and Spiking ANN

On time series representations for multi-label NILM

verfasst von: Christoforos Nalmpantis, Dimitris Vrakas

Erschienen in: Neural Computing and Applications | Ausgabe 23/2020

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Abstract

Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating. With the rise of Internet of things, running energy disaggregation models on the edge is more and more essential for privacy concerns and economic reasons. However, current NILM solutions use data-hungry deep learning models that can recognize only one device and are impossible to run on a device with limited resources. This research investigates in-depth multi-label NILM systems and suggests a novel framework which enables a cost-effective solution. It can be deployed on an embedded device, and thus, privacy can be preserved. The proposed system leverages dimensionality reduction using Signal2Vec, is evaluated on two popular public datasets and outperforms another state-of-the-art multi-label NILM system.
Vorheriger Artikel Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining
Nächster Artikel Estimating cooling production and monitoring efficiency in chillers using a soft sensor

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Literatur
1.
Agrawal R, Faloutsos C, Swami A (1993) Efficient similarity search in sequence databases. In: International conference on foundations of data organization and algorithms. Springer, pp 69–84
2.
Armel KC, Gupta A, Shrimali G, Albert A (2013) Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy 52:213–234CrossRef
3.
Baranski M, Voss J (2003) Nonintrusive appliance load monitoring based on an optical sensor. In: 2003 IEEE Bologna power tech conference proceedings, vol 4. IEEE, pp. 8
4.
Basu K, Debusschere V, Bacha S (2012) Load identification from power recordings at meter panel in residential households. In: 2012 XXth international conference on electrical machines. IEEE, pp 2098–2104
5.
Basu K, Debusschere V, Bacha S (2013) Residential appliance identification and future usage prediction from smart meter. In: IECON 2013-39th annual conference of the IEEE industrial electronics society. IEEE, pp 4994–4999
6.
Basu K, Debusschere V, Bacha S, Maulik U, Bondyopadhyay S (2014) Nonintrusive load monitoring: a temporal multilabel classification approach. IEEE Trans Ind Inform 11(1):262–270CrossRef
7.
Basu K, Debusschere V, Douzal-Chouakria A, Bacha S (2015) Time series distance-based methods for non-intrusive load monitoring in residential buildings. Energy Build 96:109–117CrossRef
8.
Batra N, Kelly J, Parson O, Dutta H, Knottenbelt W, Rogers A, Singh A, Srivastava M (2014) Nilmtk: an open source toolkit for non-intrusive load monitoring. In: Proceedings of the 5th international conference on Future energy systems. ACM, pp 265–276
9.
Batra N, Kukunuri R, Pandey A, Malakar R, Kumar R, Krystalakos O, Zhong M, Meira P, Parson O (2019) Towards reproducible state-of-the-art energy disaggregation. In: Proceedings of the 6th ACM international conference on embedded systems for energy-efficient built environments (BuildSys’ 19). ACM, New York, NY, USA
10.
Chang HH, Chien PC, Lin LS, Chen N (2011) Feature extraction of non-intrusive load-monitoring system using genetic algorithm in smart meters. In: 2011 IEEE 8th international conference on e-business engineering. IEEE, pp 299–304
11.
Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159MathSciNetMATH
12.
13.
Froehlich J, Larson E, Gupta S, Cohn G, Reynolds M, Patel S (2010) Disaggregated end-use energy sensing for the smart grid. IEEE Pervasive Comput 10(1):28–39CrossRef
14.
Gutmann MU, Hyvärinen A (2012) Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J Mach Learn Res 13:307–361MathSciNetMATH
15.
16.
Kantz H, Schreiber T (2004) Nonlinear time series analysis, vol 7. Cambridge University Press, CambridgeMATH
17.
Kato T, Cho HS, Lee D, Toyomura T, Yamazaki T (2009) Appliance recognition from electric current signals for information-energy integrated network in home environments. In: International conference on smart homes and health telematics. Springer, pp 150–157
18.
Kelly J, Knottenbelt W (2015) The UK-dale dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci Data 2:150007CrossRef
19.
Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Dimensionality reduction for fast similarity search in large time series databases. Knowl Inf Syst 3(3):263–286MATHCrossRef
20.
Kim JG, Lee B (2019) Appliance classification by power signal analysis based on multi-feature combination multi-layer lstm. Energies 12(14):2804CrossRef
21.
Klemenjak C, Makonin S, Elmenreich W (2020) Towards comparability in non-intrusive load monitoring: on data and performance evaluation. In: 2020 IEEE power & energy society innovative smart grid technologies conference (ISGT)
22.
Klemenjak C, Reinhardt A, Pereira L, Makonin S, Bergés M, Elmenreich W (2019) Electricity consumption data sets: Pitfalls and opportunities. In: Proceedings of the 6th ACM international conference on systems for energy-efficient buildings, cities, and transportation, pp 159–162
23.
Kolter JZ, Johnson MJ (2011) Redd: a public data set for energy disaggregation research. In: Workshop on data mining applications in sustainability (SIGKDD), San Diego, CA, vol 25, pp 59–62
24.
Krystalakos O, Nalmpantis C, Vrakas D (2018) Sliding window approach for online energy disaggregation using artificial neural networks. In: Proceedings of the 10th Hellenic conference on artificial intelligence. ACM, p 7
25.
Lai YX, Lai CF, Huang YM, Chao HC (2013) Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home. Inf Sci 230:39–55CrossRef
26.
Laughman C, Lee K, Cox R, Shaw S, Leeb S, Norford L, Armstrong P (2003) Power signature analysis. IEEE Power Energy Mag 1(2):56–63CrossRef
27.
Li D, Dick S (2016) Whole-house non-intrusive appliance load monitoring via multi-label classification. In: 2016 international joint conference on neural networks (IJCNN). IEEE, pp 2749–2755
28.
Li D, Dick S (2019) Residential household non-intrusive load monitoring via graph-based multi-label semi-supervised learning. IEEE Trans Smart Grid 10(4):4615–4627CrossRef
29.
Li D, Sawyer K, Dick S (2015) Disaggregating household loads via semi-supervised multi-label classification. In: 2015 annual conference of the North American fuzzy information processing society (NAFIPS) held jointly with 2015 5th world conference on soft computing (WConSC). IEEE, pp 1–5
30.
Liang J, Ng SK, Kendall G, Cheng JW (2009) Load signature study—part i: Basic concept, structure, and methodology. IEEE Trans Power Deliv 25(2):551–560CrossRef
31.
Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing sax: a novel symbolic representation of time series. Data Min Knowl Discov 15(2):107–144MathSciNetCrossRef
32.
Lin J, Khade R, Li Y (2012) Rotation-invariant similarity in time series using bag-of-patterns representation. J Intell Inf Syst 39(2):287–315CrossRef
33.
Loukas EP, Bodurri K, Evangelopoulos P, Bouhouras AS, Poulakis N, Christoforidis GC, Panapakidis I, Chatzisavvas KC (2019) A machine learning approach for nilm based on odd harmonic current vectors. In: 2019 8th international conference on modern power systems (MPS). IEEE, pp 1–6
34.
Malinowski S, Guyet T, Quiniou R, Tavenard R (2013) 1d-sax: a novel symbolic representation for time series. In: International symposium on intelligent data analysis. Springer, pp 273–284
35.
Marchiori A, Hakkarinen D, Han Q, Earle L (2010) Circuit-level load monitoring for household energy management. IEEE Pervasive Comput 10(1):40–48CrossRef
36.
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119
37.
Nadeau C, Bengio Y (2000) Inference for the generalization error. In: Advances in neural information processing systems, pp 307–313
38.
Nalmpantis C, Vrakas D (2019) Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artif Intell Rev 52(1):217–243CrossRef
39.
Nalmpantis C, Vrakas D (2019) Signal2vec: time series embedding representation. In: International conference on engineering applications of neural networks. Springer, pp 80–90
40.
Rak R, Kurgan L, Reformat M (2005) Multi-label associative classification of medical documents from medline. In: Fourth international conference on machine learning and applications (ICMLA’05). IEEE, pp 8
41.
Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333MathSciNetCrossRef
42.
Restuccia F, D’Oro S, Melodia T (2018) Securing the internet of things in the age of machine learning and software-defined networking. IEEE Internet Things J 5(6):4829–4842CrossRef
43.
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65MATHCrossRef
44.
Ruzzelli AG, Nicolas C, Schoofs A, O’Hare GM (2010) Real-time recognition and profiling of appliances through a single electricity sensor. In: 2010 7th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON). IEEE, pp 1–9
45.
Schäfer P (2015) The boss is concerned with time series classification in the presence of noise. Data Min Knowl Discov 29(6):1505–1530MathSciNetMATHCrossRef
46.
Schäfer P, Högqvist M (2012) Sfa: a symbolic Fourier approximation and index for similarity search in high dimensional datasets. In: Proceedings of the 15th international conference on extending database technology. ACM, pp 516–527
47.
Schäfer P, Leser U (2017) Fast and accurate time series classification with weasel. In: Proceedings of the 2017 ACM on conference on information and knowledge management. ACM, pp 637–646
48.
Singhal V, Maggu J, Majumdar A (2018) Simultaneous detection of multiple appliances from smart-meter measurements via multi-label consistent deep dictionary learning and deep transform learning. IEEE Trans Smart Grid 10(3):2969–2978CrossRef
49.
Srinivasan D, Ng W, Liew A (2005) Neural-network-based signature recognition for harmonic source identification. IEEE Trans Power Deliv 21(1):398–405CrossRef
50.
Suzuki K, Inagaki S, Suzuki T, Nakamura H, Ito K (2008) Nonintrusive appliance load monitoring based on integer programming. In: 2008 SICE annual conference. IEEE, pp 2742–2747
51.
Tabatabaei SM, Dick S, Xu W (2016) Toward non-intrusive load monitoring via multi-label classification. IEEE Trans Smart Grid 8(1):26–40CrossRef
52.
Takens F (1981) Detecting strange attractors in turbulence. In: Dynamical systems and turbulence, Warwick 1980. Springer, pp 366–381
53.
Tsoumakas G, Katakis I (2007) Multi-label classification: an overview. Int J Data Wareh Min 3(3):1–13CrossRef
54.
Verma S, Singh S, Majumdar A (2019) Multi label restricted Boltzmann machine for non-intrusive load monitoring. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 8345–8349
55.
Wu X, Kumar V (2009) The top ten algorithms in data mining. CRC Press, Boca RatonCrossRef
56.
Zhang C, Zhong M, Wang Z, Goddard N, Sutton C (2018) Sequence-to-point learning with neural networks for non-intrusive load monitoring. In: Thirty-second AAAI conference on artificial intelligence
57.
Zhang ML, Zhou ZH (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recognit 40(7):2038–2048MATHCrossRef
58.
Zhong M, Goddard N, Sutton C (2015) Latent Bayesian melding for integrating individual and population models. In: Advances in neural information processing systems, pp 3618–3626
59.
Zia T, Bruckner D, Zaidi A (2011) A hidden Markov model based procedure for identifying household electric loads. In: IECON 2011-37th annual conference of the IEEE industrial electronics society. IEEE, pp 3218–3223
Metadaten
Titel
On time series representations for multi-label NILM
verfasst von
Christoforos Nalmpantis
Dimitris Vrakas
Publikationsdatum
02.05.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 23/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04916-5

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