基于SOA-RVFL预测模型的建筑节能控制方法研究
摘要：为了在满足建筑室内舒适性的同时更有效地节约能耗，本文提出了一种基于海鸥算法优化的随机向量功能连接网络(SOA-RVFL)策略对建筑能耗与温度进行预测，并通过预测结果动态调节建筑内的制热/制冷系统。策略在济南某公共建筑上应用，结果与传统的基线控制相比，降低了11.9%的建筑能耗。
关键词：模型预测控制随机向量函数连接网络海鸥优化算法建筑节能
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[2] 晋远，燕达，安晶晶，李进，孙红三，吴如宏，杨一帆.基于历史在室规律的办公照明系统模型预测控制[J].建筑科学，2020,36(04):191-198.
[3] 赵安军，周梦，于军琪，孙光.室内环境品质模型预测控制与优化研究[J].控制工程，2019,26(03):570-577.
[4] 陈厚合，李泽宁，姜涛，李雪，张儒峰，李国庆.基于模型预测控制的智能楼宇用能灵活性调控策略[J].电力系统自动化，2019,43(16):116-124.
[5] J Drgoňa,Picard D,Helsen L .Cloud-based implementation of white-box model predictive control for a GEOTABS office building:A field test demonstration[J].Journal of Process Control,2020,88:63-77.
[6] Jorissen F.Toolchain for optimal control and design of energy systems in buildings[J].2018.
[7] Picard D.Modeling,optimal control and HVAC design of large buildings using ground source heat pump systems[J].2017.
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Research on Building Energy Saving Control Method Based on the SOA-RVFL Prediction Model
Abstract: In order to satisfy the indoor comfort of buildings and save energy consumption more effectively at the same time, this paper proposes a seagull optimization algorithm based random vector functional link network(SOA-RVFL) strategy to predict the building energy consumption and temperature, and dynamically adjusts the heating/cooling system in the building through the prediction results. The strategy was applied to a public building in Jinan. Results indicated that the proposed method could reduce the building energy consumption by about 11.9% in comparison with the traditional base control method.
Keywords: model predictive control; random vector functional link network; seagull optimization algorithm; building energy conservation;
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