Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory
Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory
复制标题
基于专家预测和模糊贝叶斯理论的长期用电量预测
DOI：
10.1016/j.energy.2018.10.073
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发表时间：
2019-01
期刊：
影响因子：
9
通讯作者：
Tian Shijun
中科院分区：
文献类型：
--
作者：
Tang Lei;Wang Xifan;Wang Xiuli;Shao Chengcheng;Liu Shiyu;Tian Shijun
电力系统长期用电量预测是电力系统扩容规划的重要组成部分。提出了一种基于模糊贝叶斯理论和专家预测的长期概率预测模型，用于预测2010-2030年中国人均用电量及其变化区间。这种特殊的模型结构可以通过计量经济学方法提高专家预测的可靠性和准确性。它由三部分组成：模糊关系矩阵、先验预测和模糊贝叶斯公式。为了应对长期的不确定性，从概率的角度将专家经验的优点与其他基于时间的方法相结合，实现了先验预测。利用模糊技术，将影响因素对PEC的多重影响表示为模糊关系矩阵。通过概率校正，可以使先验预测结果符合自然演化的长期均衡关系。为了验证该方法的有效性和适用性，将该方法的结果与其他6种方法和4个机构的结果进行了比较。实例研究表明，该方法具有较高的精度和适应性。
Long-term electricity consumption (EC) forecasting is a very important part for the expansion planning of power system. Instead of point forecasting, based on fuzzy Bayesian theory and expert prediction, a novel long-term probability forecasting model is proposed to predict the Chinese per-capita electricity consumption (PEC) and its variation interval over the period 2010–2030. The special model structure can improve the reliability and accuracy of expert prediction through econometric methodology. It contains three components: fuzzy relation matrix, prior prediction, and fuzzy Bayesian formula. To contend with the long-term uncertainty, the prior prediction is implemented to combine the advantages of expert's experience with other time-based methods from the perspective of probability. With the utilization of fuzzy technique, the multiple effects of influencing factors (IFs) on PEC can be expressed as a fuzzy relation matrix. It can rule the results of prior prediction to obey the long-run equilibrium relationship of natural evolution thorough probability calibration. To demonstrate its efficiency and applicability, the result of this method is compared with that of other 6 approaches and 4 agencies. The case study shows that the proposed methodology has higher accuracy and adaptability.
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