A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction
A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction
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一种集成误差的新型预测区间方法
DOI：
10.1016/j.energy.2018.08.180
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发表时间：
2018
期刊：
影响因子：
9
通讯作者：
Yongming Han
中科院分区：
文献类型：
--
作者：
Yuan Xu;Mingqing Zhang;Liangliang Ye;Qunxiong Zhu;Zhiqiang Geng;Yan-Lin He;Yongming Han
目前，石化行业装置多、设备集成度高，具有高度不确定性和非线性的特点。因此，对能量建模进行可靠、准确的点测量变得越来越困难。针对这一问题，提出了一种误差自反馈极端学习机（ESF-ELM）与粒子群优化（PSO）相结合的预测区间（PI）方法。为了提高极限学习机（ELM）的能量建模精度，输入权值采用余弦相似系数初始化，而不是随机初始化。此外，在输入层和隐层中分别加入误差反馈层和自反馈层，以提高泛化性能。最后，提出了一种综合评价粒子群算法，用于评价粒子群算法的平均覆盖概率和平均宽度百分比。将该方法应用于精对苯二甲酸生产过程能耗PI的构建。仿真结果表明，该模型能够生成覆盖概率大、宽度窄的高质量PI，具有较强的适应性和可靠性，为决策者实现利益最大化和合理规划提供了指导。
Nowadays, petrochemical industries with many integrated units and equipment have characteristics of high uncertainty and nonlinearity. Therefore, it becomes more and more difficult to make reliable and accurate point measurement of energy modeling. To tackle this problem, a novel prediction intervals (PIs) method integrating error & self-feedback extreme learning machine (ESF-ELM) with particle swarm optimization (PSO) is proposed. For improving the energy modeling accuracy of extreme learning machine (ELM), the input weights are initialized using cosine similarity coefficients but not randomly initialized. In addition, an error-feedback layer and a self-feedback layer are added to the input layer and the hidden layer for enhancing generalization performance, respectively. Finally, PSO with a comprehensive measure is developed to evaluate the mean coverage probability and the mean width percentage of PIs. The proposed ESF-ELM with PSO is applied to constructing PIs of energy consumption for a Purified Terephthalic Acid production process. Simulation results show the proposed model can generate high-quality PIs with large coverage probability, narrow width, and superiority in adaptability and reliability, which provides guidance for decision makers to maximize benefits and give reasonable future plans.