Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns

Wang, Fei; Li, Kangping; Duić, Neven; Mi, Zengqiang; Hodge, Bri‐Mathias; Shafie‐khah, Miadreza; Catalão, João P.S.

doi:10.1016/j.enconman.2018.06.017

Cited by 151 publications

(47 citation statements)

References 34 publications

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“…Several previous studies in transport and travel sciences have extracted information from massive trajectory databases, and they have tended to use sequential pattern mining [17]. Alternative data mining methods include association rule mining analysis [10,[26][27][28], which is a rule-based machine learning method. It has been used to identify groups of variables that are highly correlated with each other from an extensive database or determine patterns of relations between variables of interest [11].…”

Section: Market Basket Analysis In Tourism Studiesmentioning

confidence: 99%

Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

et al. 2019

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The rise of radiofrequency scanner technology has led to its potential application in the observation of people’s movements. This study used a Wi-Fi scanner device to track tourists’ traveling behavior in Hokkaido’s tourism area, which occupies a large region that features a unique natural landscape. Inbound tourists have significantly increased in recent years; thus, tourism’s sustainability is considered to be important for maintaining the tourism atmosphere in the long term. Using internet-enabled technology to conduct extensive area surveys can overcome the limitations imposed by conventional methods. This study aims to use digital footprint data to describe and understand traveler mobility in a large tourism area in Hokkaido. Association rule mining (ARM)—a machine learning methodology—was performed on a large dataset of transactions to identify the rules that link destinations visited by tourists. This process resulted in the discovery of traveling patterns that revealed the association rules between destinations, and the attractiveness of the destinations was scored on the basis of visiting frequency, with both inbound and outbound movements considered. A visualization method was used to illustrate the relationships between destinations and simplify the mathematical descriptions of traveler mobility in an attractive tourism area. Hence, mining the attractiveness of destinations in a large tourism area using an ARM method integrated with a Wi-Fi mobility tracking approach can provide accurate information that forms a basis for developing sustainable destination management and tourism policies.

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Section: Market Basket Analysis In Tourism Studiesmentioning

confidence: 99%

Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

et al. 2019

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“…Two indicators defined in Equations (23) and (24) are adopted to evaluate the performance of the proposed DPVSCE model:…”

Section: Performance Metric For Dpvsce Modelmentioning

confidence: 99%

“…The input feature showing more strong correlation with the output is usually considered as a more important feature [23]. Maximal information coefficient (MIC) is used to analyze the relative importance of the proposed input features for both classification and capacity estimation, which can not only quantify the linear relation but also quantify the non-linear relation between two variables [24].…”

Section: Correlation Analysismentioning

confidence: 99%

A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

Wang

et al. 2018

Energies

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Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.

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“…So, because of its high performance and simplicity, the k-means or Lloyd's algorithm is applied as non-hierarchy clustering method [39]. This algorithm finds the k centroids of the k clusters and assigns members to each cluster according to their distance to the centroid.…”

Section: Clusteringmentioning

confidence: 99%

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

et al. 2018

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Health Services building stock is usually the top energy consumer in the Administrative sector, by a considerable margin. Therefore, energy consumption supervision, prediction, and improvement should be carried out for this group in a preferential manner. Most prior studies in this field have characterized the energy consumption of buildings based on complex simulations, which tend to be limited by modelisation restrictions and assumptions. In this paper, an improved method for the clusterization of buildings based on their electrical energy consumption is proposed and, then, reference profiles are determined by examining the variation of energy consumption over the typical yearly consumption period. The temporary variation has been analyzed by evaluating the temporary evolution of the area consumption index through data mining and statistical clusterization techniques. The proposed methodology has been applied to building stock of the Health Services in the Castilla y León region in Spain, based on three years of historical monthly electrical energy consumption data for over 250 buildings. This building stock consists of hospitals, health centers (with and without emergency services) and a miscellaneous set of administrative and residential buildings. Results reveal five distinct electrical consumption profiles that have been associated with five reference buildings, permitting significant improvement in the demand estimation as compared to merely using the classical energy consumption indicators.

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Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns

Cited by 151 publications

References 34 publications

Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

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