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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参考文献[1] 王灿，张雅欣.碳中和愿景的实现路径与政策体系[J].中国环境管理，2020,12(06):58-64.
[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.
[8] Dhiman G ,Kumar V .Seagull optimization algorithm:Theory and its applications for large-scale industrial engineering problems[J].Knowledge-Based Systems,2019,165(FEB.1):169-196.
[9] 张学坤.基于SOA-SVM和SOA-BP模型的溶解氧预测[J].人民珠江，2021,42(04):99-104.
[10] Sun H,Zhai W,Wang Y,et al.Privileged information-driven random network based non-iterative integration model for building energy consumption prediction[J].Applied Soft Computing,2021:107438.
[11] Jain A,Smarra F,Reticcioli E,et al.NeurOpt:Neural network based optimization for building energy management and climate control[C]//Learning for Dynamics and Control.PMLR,2020:445-454.
[12] 王旭东，吴莉萍，戚艳，丁一，戚冯宇，杜丽佳.基于模型预测控制的智能楼宇暖通空调能量管理策略[J].电力系统及其自动化学报，2019,31(06):98-106.
[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.
[8] Dhiman G ,Kumar V .Seagull optimization algorithm:Theory and its applications for large-scale industrial engineering problems[J].Knowledge-Based Systems,2019,165(FEB.1):169-196.
[9] 张学坤.基于SOA-SVM和SOA-BP模型的溶解氧预测[J].人民珠江，2021,42(04):99-104.
[10] Sun H,Zhai W,Wang Y,et al.Privileged information-driven random network based non-iterative integration model for building energy consumption prediction[J].Applied Soft Computing,2021:107438.
[11] Jain A,Smarra F,Reticcioli E,et al.NeurOpt:Neural network based optimization for building energy management and climate control[C]//Learning for Dynamics and Control.PMLR,2020:445-454.
[12] 王旭东，吴莉萍，戚艳，丁一，戚冯宇，杜丽佳.基于模型预测控制的智能楼宇暖通空调能量管理策略[J].电力系统及其自动化学报，2019,31(06):98-106.
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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