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首页> 外文期刊>Energy Conversion & Management >A renewable energies-assisted sustainable development plan for Iran using techno-econo-socio-environmental multivariate analysis and big data
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A renewable energies-assisted sustainable development plan for Iran using techno-econo-socio-environmental multivariate analysis and big data

机译：使用技术-生态-社会-环境-多变量分析和大数据的伊朗可再生能源辅助可持续发展计划

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摘要

In the present study, sustainable development is investigated in Iran using renewable energies-assisted Techno-Econo-Socio-Environmental Multivariate Analysis (TESEMA) as a novel holistic approach. Accordingly, six annual hourly consumption variables, reported by Iran's power industry from 2011 to 2017, are predicted using seven dynamic and intelligent models. Consequently, technical and economic variables are obtained by an optimal design of hybrid solar, wind, and biogas systems at 53 sites in Iran. Thirteen social variables are studied using a technique for order-preference by similarity to an ideal solution (TOPSIS) and six hazardous air pollutants are reported in Iran using a geographic information systems interpolation tool. Then, four major TESEMA variables are used in multivariate statistical analyses to reduce the big data diversity. Principal component analysis (PCA) is performed to find a linear model among the variables, and K nearest neighborhood (KNN) algorithm is used to cluster the sites according to the modeling results. A partial least square-based regression is conducted to investigate any correlation between major variables of TESEMA and population density in Iran. Finally, TESEMA development index (DI) and facial graphs are proposed as novel numerical and graphical sustainable development monitoring techniques, respectively. The results show that DNN is the best model to predict demand load in Iran (RMSE = 73.15%). Since DI varies in a wide range from 0 to 248.83 and the population density is significantly correlated with TESEMA variables (R-2 = 91.86%), the current centralistic policies should be revised in Iran to reach sustainable development. Thus, a four-cluster management strategy accompanied by smart monitoring can efficiently lead to sustainable development in Iran.
机译：在本研究中，使用可再生能源辅助的技术-经济-社会-环境多变量分析（TESEMA）作为一种新颖的整体方法，对伊朗的可持续发展进行了研究。因此，使用七个动态和智能模型预测了伊朗电力行业从2011年至2017年报告的六个年度小时消耗变量。因此，通过对伊朗53个地点的太阳能，风能和沼气混合系统进行优化设计，可以获得技术和经济变量。使用一种类似于理想解决方案（TOPSIS）的顺序偏好技术研究了13种社会变量，并使用地理信息系统插值工具在伊朗报告了6种有害空气污染物。然后，在多元统计分析中使用了四个主要的TESEMA变量，以减少大数据的多样性。执行主成分分析（PCA）以在变量之间找到线性模型，并根据建模结果使用K最近邻域（KNN）算法对站点进行聚类。进行了基于偏最小二乘的局部回归，以研究TESEMA主要变量与伊朗人口密度之间的任何相关性。最后，提出了TESEMA发展指数（DI）和面部图，分别作为新型的数字和图形可持续发展监测技术。结果表明，DNN是预测伊朗需求负荷的最佳模型（RMSE = 73.15％）。由于直接投资在0到248.83之间变化很大，并且人口密度与TESEMA变量显着相关（R-2 = 91.86％），因此伊朗应修订当前的集中政策以实现可持续发展。因此，伴随着智能监控的四集群管理策略可以有效地促进伊朗的可持续发展。

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