با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
دسته بندی:
داده کاوی - data mining
سال انتشار:
2018
عنوان انگلیسی مقاله:
Analyze the energy consumption characteristics and affecting factors of Taiwan’s convenience stores-using the big data mining approach
ترجمه فارسی عنوان مقاله:
ویژگی های مصرف انرژی و عوامل موثر را تحلیل کنیداز فروشگاه های راحت تایوان - با استفاده از روش کاوش داده های بزرگ
منبع:
Sciencedirect - Elsevier - Energy & Buildings, 168 (2018) 120-136: doi:10:1016/j:enbuild:2018:03:021
نویسنده:
Chung-Feng Jeffrey Kuo a,∗, Chieh-Hung Lin a,b, Ming-Hao Lee b
چکیده انگلیسی:
This study applies big data mining, machine learning analysis technique and uses the Waikato Environ
ment for Knowledge Analysis (WEKA) as a tool to discuss the convenience stores energy consumption
performance in Taiwan which consists of (a). Influential factors of architectural space environment and
geographical conditions; (b). Influential factors of management type; (c). Influential factors of business
equipment; (d). Influential factors of local climatic conditions; (e). Influential factors of service area so
cioeconomic conditions. The survey data of 1,052 chain convenience stores belong to 7-Eleven, Family
Mart and Hi-Life groups by Taiwan Architecture and Building Center (TABC) in 2014. The implicit knowl
edge will be explored in order to improve the traditional analysis technique which is unlikely to build a
model for complex, inexact and uncertain dynamic energy consumption system for convenience stores.
The analysis process comprises of (a). Problem definition and objective setting; (b). Data source selection;
(c). Data collection; (d). Data preprocessing/preparation; (e). Data attributes selection; (f). Data mining
and model construction; (g). Results analysis and evaluation; (h). Knowledge discovery and dissemination.
The key factors influencing the convenience stores energy consumption and the influence intensity order
can be explored by data attributes selection. The numerical prediction model for energy consumption is
built by applying regression analysis and classification techniques. The optimization thresholds of various
influential factors are obtained. The different cluster data are compared by using clustering analysis to
verify the correlation between the factors influencing the convenience stores energy consumption char
acteristic. The implicit knowledge of energy consumption characteristic obtained by the aforesaid analysis
can be used to (a). Provide the owners with accurate predicted energy consumption performance to opti
mize architectural space, business equipment and operations management mode; (b). The design planners
can obtain the optimum design proposal of Cost Performance Ratio (C/P) by planning the thresholds of
various key factors and the validation of prediction model; (c). Provide decision support for government
energy and environment departments, to make energy saving and carbon emission reduction policies, in
order to estimate and set the energy consumption scenarios of convenience store industry.
Keywords: Convenience store ، Data mining ، Machine learning ، Energy consumption characteristics ، Energy consumption affecting factor
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0