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نتیجه جستجو - Energy consumption affecting factor

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Analyze the energy consumption characteristics and affecting factors of Taiwan’s convenience stores-using the big data mining approach
ویژگی های مصرف انرژی و عوامل موثر را تحلیل کنیداز فروشگاه های راحت تایوان - با استفاده از روش کاوش داده های بزرگ-2018
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
مقاله انگلیسی
2 Analyze the energy consumption characteristics and affecting factors of Taiwan’s convenience stores-using the big data mining approach
تجزیه و تحلیل ویژگی های مصرف انرژی و عوامل موثر در فروشگاه های راحتی تایوان با استفاده از روش کاوش داده های بزرگ-2018
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
مقاله انگلیسی
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