A subspace signature based approach for residential appliances identification using less informative and low resolution smart meter data

Dinesh, H. G. C. P.; Nettasinghe, D. B. W.; Godaliyadda, G. M. R. I.; Ekanayake, M. P. B.; Wijayakulasooriya, J. V.; Ekanayake, Janaka

doi:10.1109/iciinfs.2014.7036579

Cited by 14 publications

(8 citation statements)

References 6 publications

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“…1. The main steps are, 1) Feature extraction from individual appliance power profiles (in Reading Set 1 -RS1) by the Karhunen Loève Expansion (KLE) based technique described in [3]. 2) Creation of Appliance/Combination/Power consumption level signature databases using extracted features.…”

Section: Underlying Nilm Methods Used a Overview Of The Nilm Algomentioning

confidence: 99%

“…Even though there are many such diverse NILM approaches suggested in literature [1]- [27], [31]- [44], almost all the NILM methods estimate the present turned ON appliance combination based on the recently collected set of measurements. For example, the NILM strategy proposed in [3] decides the currently turned ON appliance combination based on ten most recent set of total active power measurements. Most of the proposed NILM methods completely rely on smart meter measurements without incorporating any of the activity that happened in the recent past in terms of load activity and inactivity.…”

Section: A Related Workmentioning

confidence: 99%

“…This is denoted simply as an appliance level Probability Mass Function (PMF) P (A1,fc=0.2Hz) (Re 1 , Im 1 ). Utilizing each of these constructed appliance specific PMFs, a set of appliance combination specific PMFs for each f c for each possible appliance combination were constructed through mathematical convolution operation between corresponding appliance specific PMFs [3]. This gives the probability of obtaining a feature when that appliance combination is currently turned ON.…”

Section: Signature Database Constructionmentioning

confidence: 99%

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Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting

Welikala

Dinesh

Ekanayake

et al. 2019

IEEE Trans. Smart Grid

139

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This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method which incorporates appliance usage patterns (AUPs) to improve performance of active load identification and forecasting. In the first stage, the AUPs of a given residence were learnt using a spectral decomposition based standard NILM algorithm. Then, learnt AUPs were utilized to bias the priori probabilities of the appliances through a specifically constructed fuzzy system. The AUPs contain likelihood measures for each appliance to be active at the present instant based on the recent activity/inactivity of appliances and the time of day. Hence, the priori probabilities determined through the AUPs increase the active load identification accuracy of the NILM algorithm. The proposed method was successfully tested for two standard databases containing real household measurements in USA and Germany. The proposed method demonstrates an improvement in active load estimation when applied to the aforementioned databases as the proposed method augments the smart meter readings with the behavioral trends obtained from AUPs. Furthermore, a residential power consumption forecasting mechanism, which can predict the total active power demand of an aggregated set of houses, five minutes ahead of real time, was successfully formulated and implemented utilizing the proposed AUP based technique.

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Section: Underlying Nilm Methods Used a Overview Of The Nilm Algomentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: Signature Database Constructionmentioning

confidence: 99%

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Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting

Welikala

Dinesh

Ekanayake

et al. 2019

IEEE Trans. Smart Grid

139

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“…First, each active power signal was split into sliding windows (SWs) of 10 samples. Then from each SW, 5 uncorrelated spectral components which are referred as subspace components (SCs) were extracted using the Karhunen Loeve Expansion (KLE) based method [18,22,37] described in Appendix A. Here, the KLE based spectral feature extraction method was used as unique signature information of active power signals might not be apparent in the time domain profiles due to their low sampling rate.…”

Section: Spectral Features Of Individual Appliancesmentioning

confidence: 99%

Non-intrusive load monitoring under residential solar power influx

et al. 2017

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This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) -USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day's total load that can be shed. * Corresponding AuthorEmail addresses: hchintha@sfu.ca; dineshhgcp@gmail.com (Chinthaka Dinesh), shirantha.welikala.2015@IEEE.org; shirantha27@gmail.com (Shirantha Welikala), yasithashehanliyanage@gmail.com; wlysliyanage@IEEE.org (Yasitha Liyanage), mpb.ekanayake@ee.pdn.ac.lk (Mervyn Parakrama B. Ekanayake), roshangodd@ee.pdn.ac.lk (Roshan Indika Godaliyadda), jbe@ee.pdn.ac.lk; EkanayakeJ@cardiff.ac.uk (Janaka Ekanayake) Proposed

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“…[11] introduces that by using clustering algorithm to deals the smart meter data realizes the resolution of load. [12] shows the subspace method to certificate the household appliances based on the smart meter data. [13] researches on the applications of the smart meter data in the user classification and load profiling.…”

Section: The Methods Of Forecasting Power Loadmentioning

confidence: 99%

On the Research of the Smart Grid Load Forecasting Cloud Platform Architecture

Zhai¹

2017

Advances in Computer Science Research

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Abstract. Load forecasting is making calculations or predictions of a future event or condition based on analysis or study of historical data, events or observations. With the development of smart grid, smart meter is also increasing largely, which can generate metering data per 15 mins, and there comes a huge amount of metering data. Thus such huge amount of data leads to some challenges in data processing. This paper introduces the meaning of studying load forecasting, and recommends current relevant research. Then, the cloud platform architecture, the key technologies based cloud and current relevant research for load forecasting based on cloud in smart grid is discussed. Finally it introduces the related load forecasting services based on the cloud platform architecture in smart grid. After all, there is a conclusion of this paper.

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A subspace signature based approach for residential appliances identification using less informative and low resolution smart meter data

Cited by 14 publications

References 6 publications

Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting

Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting

Non-intrusive load monitoring under residential solar power influx

On the Research of the Smart Grid Load Forecasting Cloud Platform Architecture

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