Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities

被引：72

作者：

Elnour, Mariam ^{[1
]}

Himeur, Yassine ^{[1
]}

Fadli, Fodil ^{[1
]}

Mohammedsherif, Hamdi ^{[1
]}

Meskin, Nader ^{[2
]}

Ahmad, Ahmad M. ^{[1
]}

Petri, Ioan ^{[3
]}

Rezgui, Yacine ^{[3
]}

Hodorog, Andrei ^{[3
]}

机构：

[1] Qatar Univ, Dept Architecture & Urban Planning, Doha, Qatar

[2] Qatar Univ, Dept Elect Engn, Doha, Qatar

[3] Cardiff Univ, BRE Inst Sustainable Engn, Sch Engn, Cardiff, ON, Canada

来源：

APPLIED ENERGY | 2022年 / 318卷

关键词：

Model predictive control; Neural networks; Energy management and optimization; Sports facilities; ENERGY-CONSUMPTION; THERMAL COMFORT; SWIMMING POOLS; IMPLEMENTATION; CHALLENGES; EFFICIENCY;

D O I：

10.1016/j.apenergy.2022.119153

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Sports facilities are considered complex buildings due to their high energy demand and occupancy profiles. Therefore, their management and optimization are crucial for reducing their energy consumption and carbon footprint while maintaining an appropriate indoor environmental quality. This work is part of the SportE3.Q project, which aims to manage and optimize the operation of sports facilities. A neural network (NN)-based model predictive control (MPC) management and optimization system is proposed for the heating, ventilation, and air conditioning (HVAC) system of a sports hall in the sports and events complex of Qatar University (QU). The proposed approach provides an integrated dynamic optimization method that accounts for future system behavior in the decision-making process, consisting of a prediction element and an optimizer. A NN is used to implement the dynamic prediction element of the MPC system and is compared with other machine learning (ML)-based models, which are support vector regression (SVR), k-nearest neighbor (kNN), and decision trees (DT). The NN-based model outperforms the other ML models with an average root mean squared error (RMSE) of around 0.06 between the actual and the predicted values, and an average R of 0.99 as NNs are popular for their high accuracy and reliability. Two schemes of the proposed NN-based MPC system are investigated for managing and optimizing the operation of the hall's HVAC system for enhanced energy use and indoor environment quality, as wel l as for providing occupancy profile recommendations to aid the facilities' managers in handling their operation. In alignment with the objective of the SportE3.Q project, up to 46% energ y reduction was achieved while jointly optimizing the thermal comfort and indoor air quality. In addition, Scheme 2 of the proposed system provided productive occupancy recommendations for a healthier indoor environment.

引用

页数：25