Residential Load Pattern Analysis for Smart Grid Applications Based on Audio Feature EEUPC

The smart grid is an important application field of the Internet of things. This paper presents a method of user electricity consumption pattern analysis for smart grid applications based on the audio feature EEUPC. A novel similarity function based on EEUPC is adapted to support clustering analysis of residential load patterns. The EEUPC similarity exploits features of peaks and valleys on curves instead of directly comparing values and obtains better performance for clustering analysis. Moreover, the proposed approach performs load pattern clustering, extracts a typical pattern for each cluster, and gives suggestions toward better power consumption for each typical pattern. Experimental results demonstrate that the EEUPC similarity is more consistent with human judgment than the Euclidean distance and higher clustering performance can be achieved for residential electric load data.

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Residential Load Pattern Analysis for Smart Grid Applications Based on Audio Feature EEUPC

International Journal of Advanced Pervasive and Ubiquitous Computing ◽

10.4018/japuc.2011040106 ◽

2011 ◽

Vol 3 (2) ◽

pp. 39-53 ◽

Cited By ~ 3

Author(s):

Yunzhi Wang ◽

Xiangdong Wang ◽

Yueliang Qian ◽

Haiyong Luo ◽

Fujiang Ge ◽

...

Keyword(s):

Smart Grid ◽

Clustering Analysis ◽

Pattern Analysis ◽

Consumption Pattern ◽

Similarity Function ◽

Typical Pattern ◽

Pattern Clustering ◽

Audio Feature ◽

Load Pattern ◽

Grid Applications

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Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Energies ◽

10.3390/en13164154 ◽

2020 ◽

Vol 13 (16) ◽

pp. 4154 ◽

Cited By ~ 4

Author(s):

Anthony Faustine ◽

Lucas Pereira

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Energy Demand ◽

Learning Strategy ◽

Demand Management ◽

Management Strategies ◽

Electricity Consumption ◽

Consumption Pattern ◽

Similarity Function ◽

Active Components

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

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