Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study

2022 ◽  
Vol 308 ◽  
pp. 118335
Author(s):  
Xiaohai Zhang ◽  
José Luis Ramírez-Mendiola ◽  
Mingtao Li ◽  
Liejin Guo
2013 ◽  
Vol 4 (2) ◽  
pp. 1048-1057 ◽  
Author(s):  
Hideitsu Hino ◽  
Haoyang Shen ◽  
Noboru Murata ◽  
Shinji Wakao ◽  
Yasuhiro Hayashi

Author(s):  
Yunzhi Wang ◽  
Xiangdong Wang ◽  
Yueliang Qian ◽  
Haiyong Luo ◽  
Fujiang Ge ◽  
...  

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.


2021 ◽  
Author(s):  
Mrityunjoy M. Karmakar

As the electricity charges are increasing day by day & electricity or power is the first & foremost requirement to light up & ventilate any room. Running an institution or even a house with the old technology makes less sense which shall be more so in the coming years.The Chemistry department of St. Francis de Sales College, Seminary hills, Nagpur has been taken up as a case study to impart suggestions for improving electricity consumption pattern & reap benefits in the long run. The changes necessitated shall definitely lead to better progress & shall lead to national development. A case study has been presented here in the location-Department of Chemistry,St. Francis de Sales College, Seminary hills, Nagpur, Old building, A wing, III Floor.


Author(s):  
Yuan Jin ◽  
Da Yan ◽  
Xingxing Zhang ◽  
Mengjie Han ◽  
Xuyuan Kang ◽  
...  

Author(s):  
W. Huang ◽  
S. Li ◽  
S. Xu

How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. <br><br> In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. <br><br> Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the approximate 55% spatiotemporal clusters distributed in different locations can be eventually grouped as the same type of clusters with consideration of semantic aspect.


Author(s):  
W. Huang ◽  
S. Li ◽  
S. Xu

How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. <br><br> In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. <br><br> Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the approximate 55% spatiotemporal clusters distributed in different locations can be eventually grouped as the same type of clusters with consideration of semantic aspect.


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