文献类型：期刊文献

英文题名：Electric heating control of agricultural greenhouse in winter using an embedded technology based chaotic particle swarm optimization PID controller

作者：Wang, Xiaoyu Wang, Chunyan Liu, Zhennan Kang, Yi Wang, Zhenlong

第一作者：Wang, Xiaoyu

通信作者：Wang, XY[1]

机构：[1]Anhui Agr Univ, Coll Engn, Hefei 230061, Peoples R China;[2]Guizhou Inst Technol, Guiyang 550003, Peoples R China;[3]Anhui & Huaihe River Inst Hydraul Res, Key Lab Water Conservancy & Water Resources Anhui, Bengbu 233000, Peoples R China

第一机构：Anhui Agr Univ, Coll Engn, Hefei 230061, Peoples R China

通信机构：corresponding author), Anhui Agr Univ, Coll Engn, Hefei 230061, Peoples R China.

年份：2025

卷号：164

外文期刊名：INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER

收录：;EI(收录号：20251017983961);Scopus(收录号：2-s2.0-85219121020);WOS:【SCI-EXPANDED(收录号:WOS:001442082200001)】；

基金：This work has been financially supported by the National Natural Science Foundation of China, China (52209001, U2243228) , the Anhui Agricultural University Stabilization and Introduce Talents Research Foundation, China (rc412107) , and Anhui Agricultural University Youth Science Foundation, China (2021zd04) , the Universities Natural Science Research Project of Anhui Province of China (2024AH050460) . The surface meteorology and solar energy data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program.

语种：英文

外文关键词：Agricultural greenhouse; Chaotic logistic mapping; Fan coil unit; Computational fluid dynamics; Heating control system

摘要：The agricultural greenhouse is a complicated system with changeable multi-factors. To eliminate the uncertainty of systems, intelligent algorithms are widely used to optimize the framework of the proportional, integral, and derivative (PID) controller. A particle swarm optimization with chaotic logistic mapping (CPSO) is proposed to calibrate PID parameters. The CPSO-PID is implanted into the heating control system by embedded technology (ET) to improve the energy savings and system performance of the greenhouse in winter. Computational fluid dynamic (CFD) calculates the heat and mass transfers to describe the temperature distribution. It also serves as an offline energy demand predictor to cooperate with a three-stage fan coil unit (FCU) loops online response strategy to control the heating system. The determination coefficient R2 of 0.874 of the fitting results verifies that the CFD simulation reached the application level. An interference case shows the robustness of this method. In the full-scale experiments, compared with the GA-PID and PSO-PID controllers, its energy savings are 1.65 % and 8.20 % with a lower mean temperature deviation of 0.63 degrees C and 0.53 degrees C, respectively. These results show that the proposed control method can improve heating system performance with more suitable temperature, stronger adaptive capacity, faster response time, and lower energy consumption.