Energy Sources, Part B: Economics, Planning, and Policy ( IF 3.9 ) Pub Date : 2019-02-09 , DOI: 10.1080/15567249.2019.1572835 Ersin Korkmaz 1 , Ali Payıdar Akgüngör 1
This study proposes a new optimization technique to estimate the Transportation Energy Demand (TED) employing the Flower Pollination Algorithm (FPA). The TED estimation models were developed based on three parameters, which are Annual Vehicle-Km (AVK), Gross Domestic Product per Capita (GDPperC), and Carbon-dioxide (CO2) emission according to the linear, power and quadratic forms. These three parameters were determined by the WEKA data mining software program among nine parameters. Randomly selected 80% of historical data for 47 years, from 1970 to 2016, were used for the training of the algorithm, and the remains were used in the testing stage of the models. The performances of the models were evaluated according to six different statistical criteria. Transportation energy demand forecasts by 2035 were carried out using three different scenarios using the TED estimation models. According to the scenarios, it is predicted that the transportation energy demand in Turkey will have doubled by 2035 in comparison with 2016. The FPA approach has been successfully applied in the development of the TED estimation models. The most important impact of this study is to help the creation of strategic action plans for energy policies in the transport sector and to contribute to the more efficient use of limited energy resources in the country.
中文翻译:
花粉授粉算法在土耳其交通能源需求估算中的应用:模型开发与应用
这项研究提出了一种新的优化技术,利用花粉传粉算法(FPA)估算运输能源需求(TED)。TED估算模型是基于以下三个参数开发的:年度车辆Km(AVK),人均国内生产总值(GDPperC)和二氧化碳(CO 2)根据线性,幂和二次形式发射。这三个参数是由WEKA数据挖掘软件程序在9个参数中确定的。从1970年到2016年的47年中,随机选择80%的历史数据用于算法的训练,其余的用于模型的测试阶段。根据六种不同的统计标准评估了模型的性能。到2035年,运输能源需求预测是使用TED估算模型使用三种不同的方案进行的。根据这些情景,预计到2035年土耳其的运输能源需求将比2016年翻一番。FPA方法已成功应用于TED估算模型的开发中。