Data mining based framework for exploring household electricity consumption patterns: A case study in China context

Abstract This study proposes a data mining based framework for exploring the electricity consumption patterns, which includes three consecutive stages. Firstly, electricity consumption patterns and behaviors are explored in festivals such as the Spring Festival, the Labor Day and the National Day. Secondly, seasonal electricity consumption patterns and behaviors are compared, and the relationship between temperature and electricity demand is analyzed through data visualization. Thirdly, we focus on the phenomenon of electricity consumption patterns shifting. Finally, a case study of Nanjing and Yancheng City, Jiangsu Province, China is presented. The results indicate that: (1) Volatility of electricity consumption is higher in winter and summer than in spring and autumn. (2) There are three typical load profiles during the Spring Festival, two typical load profiles during the Labor Day the National Day. (3) High temperature in summer and low temperature in winter have obvious influence on electricity consumption. However, the electricity consumption peak lags one or two days behind the temperature peak in summer, and consumers’ response time gets shorter as the frequency of temperature peaks increase. (4) The phenomenon of instability of household electricity consumption patterns is identified. 7.22% of the high volatility households transferred to low volatility households from winter to spring. 6.08% low volatility households transferred to high volatility households from summer to autumn. Finally, we proposed some suggestions for promoting energy conservation and improving energy efficiency.

[1]  Liwei Liu,et al.  China׳s carbon-emissions trading: Overview, challenges and future , 2015 .

[2]  Jane You,et al.  From cluster ensemble to structure ensemble , 2012, Inf. Sci..

[3]  Luisa F. Cabeza,et al.  Investigating greenhouse challenge from growing trends of electricity consumption through home appliances in buildings , 2014 .

[4]  Zaid Chalabi,et al.  Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes , 2016 .

[5]  Laurie Thorp,et al.  Energy conservation attitudes, knowledge, and behaviors in science laboratories , 2012 .

[6]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[7]  Konstantinos Chalvatzis,et al.  ICT entertainment appliances’ impact on domestic electricity consumption , 2017 .

[8]  Theofilos A. Papadopoulos,et al.  Pattern recognition algorithms for electricity load curve analysis of buildings , 2014 .

[9]  Ling Tang,et al.  Economic and Environmental Effects of Coal Resource Tax Reform in China: Based on a Dynamic CGE Approach , 2015, ITQM.

[10]  Francisco C. Pereira,et al.  Using pattern recognition to identify habitual behavior in residential electricity consumption , 2012 .

[11]  Angele Reinders,et al.  Comparison of two residential Smart Grid pilots in the Netherlands and in the USA, focusing on energy performance and user experiences , 2017 .

[12]  Michael Conlon,et al.  A clustering approach to domestic electricity load profile characterisation using smart metering data , 2015 .

[13]  P. Narayan,et al.  Is carbon emissions trading profitable , 2015 .

[14]  David Wallom,et al.  Clustering disaggregated load profiles using a Dirichlet process mixture model , 2015 .

[15]  Benjamin C. McLellan,et al.  China’s coal consumption declining—Impermanent or permanent? , 2018 .

[16]  Ari Nissinen,et al.  Tailored advice and services to enhance sustainable household consumption in Finland , 2016 .

[17]  Gerjo Kok,et al.  Changing energy-related behavior: An Intervention Mapping approach , 2011 .

[18]  I. Rowlands,et al.  Aggregated and disaggregated correlations of household electricity consumption with time-of-use shifting and conservation , 2017 .

[19]  Shanlin Yang,et al.  Understanding household energy consumption behavior: The contribution of energy big data analytics , 2016 .

[20]  James W. Taylor,et al.  Triple seasonal methods for short-term electricity demand forecasting , 2010, Eur. J. Oper. Res..

[21]  Tara C. Kandpal,et al.  Analysis of electricity consumption under a photovoltaic micro-grid system in India , 2015 .

[22]  Kristin Andersson,et al.  Energy behaviors at the office: An intervention study on the use of equipment , 2015 .

[23]  Mikko Kolehmainen,et al.  Feature-Based Clustering for Electricity Use Time Series Data , 2009, ICANNGA.

[24]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[25]  Ming Zhang,et al.  Measuring the capacity utilization of the coal sector and its decoupling with economic growth in China’s supply-side reform , 2018 .

[26]  Yan Zhang,et al.  China's low-carbon industrial transformation assessment based on Logarithmic Mean Divisia Index model , 2016 .

[27]  Shanlin Yang,et al.  Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies , 2018 .

[28]  C. Vlek,et al.  A review of intervention studies aimed at household energy conservation , 2005 .

[29]  Danijela Nikolić,et al.  The impact of the mean daily air temperature change on electricity consumption , 2015 .

[30]  Marie Bessec,et al.  The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach , 2008 .

[31]  Matteo De Felice,et al.  Seasonal climate forecasts for medium-term electricity demand forecasting , 2015 .

[32]  Nil Gunsel Resatoglu,et al.  Energy Consumption, Electricity, and GDP Causality; The Case of Russia, 1990-2011 , 2016 .

[33]  Zhengyan Shao,et al.  On electricity consumption and economic growth in China , 2017 .

[34]  Grigorios L. Kyriakopoulos,et al.  Energy saving: Views and attitudes of students in secondary education , 2015 .

[35]  Mark Sumner,et al.  On the Energy Demands of Small Appliances in Homes , 2015 .

[36]  Chrisovaladis Malesios,et al.  Electricity curtailment behaviors in Greek households: Different behaviors, different predictors , 2014 .

[37]  Thomas Dietz,et al.  Measuring household energy efficiency behaviors with attention to behavioral plasticity in the United States , 2015 .

[38]  Vidyasagar Potdar,et al.  Energy conservation in a smart home , 2011, 5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011).

[39]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

[40]  Amit Garg,et al.  An assessment of household electricity load curves and corresponding CO2 marginal abatement cost curves for Gujarat state, India , 2014 .

[41]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[42]  Yoshikuni Yoshida,et al.  Determining the relationship between a household’s lifestyle and its electricity consumption in Japan by analyzing measured electric load profiles , 2016 .

[43]  Eri Nakamura,et al.  The effect of demand response on electricity consumption under the existence of the reference price effect: Evidence from a dynamic pricing experiment in Japan , 2018 .

[44]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[45]  Beverly K Wilson,et al.  Urban form and residential electricity consumption: Evidence from Illinois, USA , 2013 .

[46]  Changhui Yang,et al.  Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China , 2017 .

[47]  Boqiang Lin,et al.  Factors influencing renewable electricity consumption in China , 2016 .