Dataset for Ranking of Renewable Energy Sources Using Delphi-MGDM Framework

The data sets are part of the study titled "A web-based Delphi multi-criteria group decision-making framework for renewable energy project development processes." The study aims to outline and implement the web-based Delphi Multi-criteria Group Decision Making (Delphi-MGDM) Framework, which is intended to facilitate top-level group decision-making for renewable energy project development and long-term strategic direction setting. The datasets include: (1) the weights of criteria obtained from judgments of the experts, (2) the summary of criteria scores, (3) the comparison table dataset, and (4) the full report of the Visual PROMETHEE. “Criteria Weighing Dataset” is obtained from the judgment of experts using the AHP-Online System created by Klaus D. Goepel (available at https://bpmsg.com/ahp/ahp.php). On a pairwise comparison basis, we asked the experts to make their opinion on four (4) criteria and then the sixteen (16) sub-criteria in three rounds. The group weights after the third round are considered the final weights of criteria and sub-criteria. To rank RES using MCDA, we used the data from the literature and the Philippines’ DOE for all ten quantitative sub-criteria. However, there are six qualitative sub-criteria, so we asked the opinion of experts on how solar, wind, biomass, and hydro-power are performing in each criterion based on their knowledge and expertise. This time, we used a self-derived questionnaire and as a summary of this process, we produced the “Scoring of Options Dataset.” We got the average, minimum and maximum values of the scores to make data for the ranking in three cases (realistic, pessimistic, and optimistic). "Comparison table" dataset is composed of comparison tables for the three cases. Table A reflects the data for realistic case in which we use the averages of the qualitative inputs from experts, the averages of quantitative data obtained in ranges, and the actual value of data not given in ranges. Table B reflects the data for the optimistic case. For qualitative data, we used the minimum value of the sub-criteria to be minimized and maximum value for sub-criteria to maximized. For quantitative data in ranges, we used the minimum value of cost sub-criteria and maximum value of benefit sub-criteria. We estimated fictitious data for some quantitative data not given in ranges. Table C reflects the data for the pessimistic case. We used the same concept with Table B, but with opposite choices. For instance, we used the maximum value of cost sub-criteria and minimum value of benefit sub-criteria for quantitative data. Finally, we used Visual PROMETHEE (available at http://www.promethee-gaia.net/vpa.html) to rank renewable energy sources. The "Visual PROMETHEE Full Report" dataset is the actual report exported from the Visual PROMETHEE application – containing a partial and complete ranking of RES.本数据集隶属于题为《面向可再生能源项目开发流程的基于网络的德尔菲多准则群体决策框架》的研究。该研究旨在构建并实施基于网络的德尔菲多准则群体决策（Delphi-MGDM）框架，以助力可再生能源项目开发层面的顶层群体决策与长期战略方向制定。 本数据集包含以下四类内容：(1) 专家研判所得的准则权重数据，(2) 准则得分汇总数据，(3) 对比表数据集，(4) 可视化PROMETHEE（Visual PROMETHEE）完整报告。 “准则权重数据集”由专家通过Klaus D. Goepel开发的层次分析法在线系统（AHP-Online System，网址：https://bpmsg.com/ahp/ahp.php）生成。研究采用两两比较的方式，邀请专家先后针对4项准则及16项子准则开展三轮研判，三轮研判后的群体权重将作为准则与子准则的最终权重。 为通过多准则决策分析（Multi-criteria Decision Analysis, MCDA）对可再生能源发电技术（Renewable Energy Sources, RES）进行排序，研究采用了公开文献数据与菲律宾能源部（Department of Energy, DOE）提供的10项定量子准则相关数据。针对剩余6项定性子准则，研究邀请专家结合自身专业知识，对太阳能、风能、生物质能及水力发电在各项准则下的表现进行评分。本次调研采用自主设计的问卷，最终生成“备选方案评分数据集”。研究通过计算评分的平均值、最小值与最大值，得到三种场景（现实场景、悲观场景与乐观场景）下的排序所需数据。 “对比表数据集”包含三种场景对应的对比表。表A对应现实场景，其数据来源包括：专家定性研判的平均值、区间型定量数据的平均值，以及非区间型定量数据的实际值。表B对应乐观场景：对于定性数据，采用需最小化的子准则的最小值、需最大化的子准则的最大值；对于区间型定量数据，采用成本类子准则的最小值与收益类子准则的最大值；针对部分未给出区间的定量数据，研究估算了虚拟数据。表C对应悲观场景，其数据处理逻辑与表B相反：例如对于定量数据，采用成本类子准则的最大值与收益类子准则的最小值。 最后，研究采用可视化PROMETHEE（Visual PROMETHEE，网址：http://www.promethee-gaia.net/vpa.html）工具对可再生能源发电技术进行排序。“可视化PROMETHEE完整报告”数据集即从该工具导出的原始报告，其中包含可再生能源发电技术的局部与完整排序结果。