Time series distance-based methods for non-intrusive load monitoring in residential buildings

2015 ◽  
Vol 96 ◽  
pp. 109-117 ◽  
Author(s):  
Kaustav Basu ◽  
Vincent Debusschere ◽  
Ahlame Douzal-Chouakria ◽  
Seddik Bacha
Keyword(s):  
Time Series ◽  
Residential Buildings ◽  
Load Monitoring

scholarly journals Problems of dynamic modeling of the commissioning of residential buildings in the post-Soviet countries

2021 ◽  
Vol 16 (91) ◽  
pp. 40-51
Author(s):  
Olga V. Ledneva ◽  
◽  
Alexander P. Tsypin ◽  
Keyword(s):  
Time Series ◽  
Characteristic Point ◽  
Residential Buildings ◽  
Complex Structure ◽  
Complex Model ◽  
Housing Construction ◽  
Scientific Publications ◽  
Fourth Degree ◽  
Soviet Space ◽  
Post Soviet Space

The article is devoted to the description of procedures of economic and mathematical modeling of trends in the field of housing construction taking into account the peculiarities of various countries of the post-Soviet space. The results of analysis of well-known scientific publications on forecasting the dynamics of housing market indicators are presented. It has been shown that most domestic and foreign scientists as the most effective methods of modeling these indicators consider methods of analyzing time trends, in which polynomials of high (in some cases up to the fourth degree) order are used to approximate the available retrospective data. Other common approaches to solving this problem are the use of short-term forecasting based on moving average algorithms, as well as the use of the SARIMA model, which takes into account the trend and seasonal wave. The article shows that these methods do not fully take into account the profound changes in the construction complexes of the post-Soviet states caused by the significant structural transformation of their socio-economic systems. The authors proposed to use econometric models based on regressions with dummy variables to model the main indicators of housing construction, taking into account the complex structure of the external and internal environment of national construction complexes. It has been shown that in a significant number of practical situations, a fairly simple but effective way to take into account the components of the time series of the indicators under consideration in one complex model is to use the model of "change in growth (fall)" when choosing the time of the beginning (end) of a crisis situation as a characteristic point. The results of modeling the main indicators of housing construction for various countries of the post-Soviet space showed that the proposed model when constructing the medium-term forecast allows taking into account the situation component of the analyzed time series.

User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings

2011 ◽  
Vol 25 (6) ◽  
pp. 471-480 ◽  
Author(s):  
Mario Berges ◽  
Ethan Goldman ◽  
H. Scott Matthews ◽  
Lucio Soibelman ◽  
Kyle Anderson
Keyword(s):  
Residential Buildings ◽  
Load Monitoring ◽  
Electricity Load ◽  
User Centered

scholarly journals Automatic Time Series Segmentation as the Basis for Unsupervised, Non-Intrusive Load Monitoring of Machine Tools

Procedia CIRP ◽  
2019 ◽  
Vol 81 ◽  
pp. 695-700 ◽  
Author(s):  
J-P. Seevers ◽  
J. Johst ◽  
T. Weiß ◽  
H. Meschede ◽  
J. Hesselbach
Keyword(s):  
Time Series ◽  
Machine Tools ◽  
Time Series Segmentation ◽  
Load Monitoring

scholarly journals Analisis pengaruh karakteristik rumah tangga, modal manusia, modal material terhadap kemiskinan rumah tangga di 34 provinsi di Indonesia tahun 2019-2020

2021 ◽  
Vol 1 (7) ◽  
pp. 648-660
Author(s):  
Jatri Endah Nur Muliyanawati ◽  
Nasikh Nasikh
Keyword(s):  
Time Series ◽  
Human Capital ◽  
Structural Equation Modeling ◽  
Capita Income ◽  
Structural Equation ◽  
Residential Buildings ◽  
Equation Modeling ◽  
Per Capita Income ◽  
The Status ◽  
Per Capita

Abstract this Study aims to describe the influence of the Characteristics of the Households that consist of Per Capita Income and Number of Family Members, Human Capital consists of the Inhabitants of Using a Computer and the Status of Education, Capital of the Material, which consists of the Area of the Floor of the House and the Status of the Ownership of residential Buildings Against Poverty Households that consist of a Percentage indicator of Poor people, The Depth of Poverty, the Poverty Severity Index in 34 Provinces in Indonesia in the Year 2019-2020. The design and kinds of research used in this research is using quantitative methods. The Data in this research are secondary data by using time series data. Data collection was done by means of documentation. In this study using the method of the analysis of SEM (Structural Equation Modeling). Based on the results of the analysis show that the Characteristics of the Households that consist of Per Capita Income and Number of Family Members has no significant effect on Household Poverty. Human capital consists of the Inhabitants of Using a Computer and the Status of the Education has no significant effect on Household Poverty. Material capital which consists of the Area of the Floor of the House and the Status of Ownership of the residential Buildings have a significant effect on Poverty of the Household in the year 2019, and no significant effect in 2020. Abstrak Penelitian ini bertujuan untuk mendeskripsikan pengaruh Karakteristik Rumah Tangga yang terdiri dari Pendapatan Per Kapita dan Jumlah Anggota Keluarga, Modal Manusia yang terdiri dari Penduduk Menggunakan Komputer dan Status Pendidikan, Modal Material yang terdiri dari Luas Lantai Rumah dan Status Kepemilikan Bangunan Tempat Tinggal Terhadap Kemiskinan Rumah Tangga yang terdiri dari indikator Persentase Penduduk Miskin, Indeks  Kedalaman Kemiskinan, Indeks Keparahan Kemiskinan di 34 Provinsi di Indonesia Tahun 2019-2020. Rancangan dan macam penelitian yang digunakan dalam penelitian ini yaitu menggunakan metode kuantitatif. Data dalam penelitian ini yakni data sekunder dengan menggunakan data time series. Pengumpulan data dilakukan dengan cara dokumentasi. Dalam penelitian ini menggunakan metode analisis SEM (Structural Equation Modeling). Berdasarkan hasil analisis diketahui bahwa Karakteristik Rumah Tangga yang terdiri dari Pendapatan Per Kapita dan Jumlah Anggota Keluarga tidak berpengaruh signifikan terhadap Kemiskinan Rumah Tangga. Modal Manusia yang terdiri dari Penduduk Menggunakan Komputer dan Status Pendidikan tidak berpengaruh signifikan terhadap Kemiskinan Rumah Tangga. Modal Material yang terdiri dari Luas Lantai Rumah dan Status Kepemilikan Bangunan Tempat Tinggal berpengaruh signifikan terhadap Kemiskinan Rumah Tangga pada tahun 2019, dan tidak berpengaruh signifikan pada tahun 2020.

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