文献类型：期刊文献

英文题名：Data-driven two-stage stochastic programming for utility system optimization under uncertainty

作者：Ma, Guofu[1]; Zhao, Liang[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, 200237, China

年份：2022

外文期刊名：4th International Conference on Industrial Artificial Intelligence, IAI 2022

收录：EI(收录号：20230313399706)

语种：英文

外文关键词：Statistics - Stochastic programming - Stochastic systems

摘要：The utility system is a popular research field in process optimization. At the same time, widespread uncertainties pose new challenges to this issue. This paper presents a data-driven two-stage stochastic programming (TSSP) to hedge against uncertainty. A kernel density estimation (KDE) method is used to calculate the probability density function from uncertain data. Based on the derived probability density function, Latin Hypercube Sampling (LHS) samples 8-dimension uncertain data to generate different scenarios. Lastly, a real-world case study is conducted to demonstrate the effectiveness of the approach. ? 2022 IEEE.