Event-Detection Algorithms for Low Sampling Nonintrusive Load Monitoring Systems Based on Low Complexity Statistical Features

被引:63
作者
Rehman, Attique Ur [1 ]
Lie, Tek Tjing [2 ]
Valles, Brice [3 ]
Tito, Shafiqur Rahman [4 ]
机构
[1] Auckland Univ Technol, Dept Elect & Elect Engn, Auckland 1010, New Zealand
[2] Auckland Univ Technol, Sch Engn Comp & Math Sci, Auckland 1010, New Zealand
[3] Genesis Energy Ltd, Auckland 1051, New Zealand
[4] Manukau Inst Technol, Auckland 2023, New Zealand
关键词
Energy monitoring; event detection; nonintrusive load monitoring (NILM); smart grids (SGs); DISAGGREGATION; CONSUMPTION;
D O I
10.1109/TIM.2019.2904351
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
One of the key techniques toward energy efficiency and conservation is nonintrusive load monitoring (NILM) which lies in the domain of energy monitoring. Event detection is a core component of event-based NILM systems. This paper proposes two new low-complexity and computationally fast algorithms that detect the variations of load data and return the time occurrences of the corresponding events. The proposed algorithms are based on the phenomenon of a sliding window (SW) that tracks the statistical features of the acquired aggregated load data. The performance of the proposed algorithms is evaluated using real-world data and a comparative analysis has been carried out with one of the recently proposed event detection algorithms. Based on the simulations and sensitivity analysis, it is shown that the proposed algorithm can provide the results of up to 93% and 88% in terms of recall and precision, respectively.
引用
收藏
页码:751 / 759
页数:9
相关论文
共 40 条
[1]   Unsupervised approach for load disaggregation with devices interactions [J].
Aiad, Misbah ;
Lee, Peng Hin .
ENERGY AND BUILDINGS, 2016, 116 :96-103
[2]   Event-Based Energy Disaggregation Algorithm for Activity Monitoring From a Single-Point Sensor [J].
Alcala, Jose ;
Urena, Jesus ;
Hernandez, Alvaro ;
Gualda, David .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2017, 66 (10) :2615-2626
[3]  
Altrabalsi H., 2014, 2014 IEEE S COMPUTAT, P1, DOI [DOI 10.1109/CIASG.2014.7011569, 10.1109/CIASG.2014.7011569]
[4]   Low-complexity energy disaggregation using appliance load modelling [J].
Altrabalsi, Hana ;
Stankovic, Vladimir ;
Liao, Jing ;
Stankovic, Lina .
AIMS ENERGY, 2016, 4 (01) :1-21
[5]   Load Demand Disaggregation based on Simple Load Signature and User's Feedback [J].
Amenta, Valeria ;
Tina, Giuseppe Marco .
SUSTAINABILITY IN ENERGY AND BUILDINGS: PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE SEB-15, 2015, 83 :380-388
[6]  
Anderson KD, 2012, IEEE IND ELEC, P3312, DOI 10.1109/IECON.2012.6389367
[7]  
[Anonymous], 1997, TR198918V1 EL POW RE, V1
[8]  
[Anonymous], 1984, Tech. Rep.
[9]  
[Anonymous], 2012, Artificial Intelligence and Statistics
[10]   Is disaggregation the holy grail of energy efficiency? The case of electricity [J].
Armel, K. Carrie ;
Gupta, Abhay ;
Shrimali, Gireesh ;
Albert, Adrian .
ENERGY POLICY, 2013, 52 :213-234