TY - JOUR
T1 - Development and performance evaluation of a chiller plant predictive operational control strategy by artificial intelligence
AU - Chan, K. C.
AU - Wong, Victor T.T.
AU - Yow, Anthony K.F.
AU - Yuen, P. L.
AU - Chao, Christopher Y.H.
N1 - Funding Information:
This work was supported by the Contract Research (project no. 1296EM19C) from the Electrical and Mechanical Services Department of the Hong Kong Special Administrative Region, China.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Traditionally, chiller plants are controlled and monitored by a predetermined control strategy to ensure appropriate operation based on the designed system configuration. With the use of new technology of variable speed drive (VSD) for compressors, smart control strategies could be leveraged to enhance the system efficiency in lieu of traditional control strategies. For example, using orderly and straightforward switching procedures without considering various factors in switching the units, including the high-efficiency partial load range benefitted from the VSD, the actual performance of the units as a whole and the variable chilled water flow rate, result in the chiller plant not operating at maximum performance and efficiency. To address these issues, a hybrid predictive operational chiller plant control strategy is proposed to optimize the performance of the chiller plant. Artificial intelligence is employed as the data mining algorithm, with big data analysis based on the actual acquired voluminous operation data by fully considering the characteristics of chiller plants without additional installation of large-sized and high-priced equipment. Artificial neural network (ANN) was employed in the control strategy to predict the future outdoor temperature, building cooling load demand and the corresponding power consumption of the chiller plants. At the same time, particle swarm optimization (PSO) was applied to search for the optimized setpoints, e.g., chilled water supply temperature, operating sequence, chilled water flow rate, for the chiller plants. The developed control strategy has been launched in a chiller plant with a cooling capacity of 7,700 kW installed in a hospital in Hong Kong. The system coefficient of performance (COP) and overall energy consumption of the chiller plants were enhanced by about 8.6% and reduced by about 7.9%, respectively, compared with the traditional control strategy. This real-time, continuous, automatic optimization control strategy can determine the most efficient combination of operating parameters of a chiller plant with different control settings. This ensures that the chiller plant operates in its most efficient mode year-round under various operational conditions.
AB - Traditionally, chiller plants are controlled and monitored by a predetermined control strategy to ensure appropriate operation based on the designed system configuration. With the use of new technology of variable speed drive (VSD) for compressors, smart control strategies could be leveraged to enhance the system efficiency in lieu of traditional control strategies. For example, using orderly and straightforward switching procedures without considering various factors in switching the units, including the high-efficiency partial load range benefitted from the VSD, the actual performance of the units as a whole and the variable chilled water flow rate, result in the chiller plant not operating at maximum performance and efficiency. To address these issues, a hybrid predictive operational chiller plant control strategy is proposed to optimize the performance of the chiller plant. Artificial intelligence is employed as the data mining algorithm, with big data analysis based on the actual acquired voluminous operation data by fully considering the characteristics of chiller plants without additional installation of large-sized and high-priced equipment. Artificial neural network (ANN) was employed in the control strategy to predict the future outdoor temperature, building cooling load demand and the corresponding power consumption of the chiller plants. At the same time, particle swarm optimization (PSO) was applied to search for the optimized setpoints, e.g., chilled water supply temperature, operating sequence, chilled water flow rate, for the chiller plants. The developed control strategy has been launched in a chiller plant with a cooling capacity of 7,700 kW installed in a hospital in Hong Kong. The system coefficient of performance (COP) and overall energy consumption of the chiller plants were enhanced by about 8.6% and reduced by about 7.9%, respectively, compared with the traditional control strategy. This real-time, continuous, automatic optimization control strategy can determine the most efficient combination of operating parameters of a chiller plant with different control settings. This ensures that the chiller plant operates in its most efficient mode year-round under various operational conditions.
KW - Artificial intelligence
KW - Artificial neural network
KW - Building energy saving
KW - Chiller plant optimization
KW - Particle swarm optimization
KW - VSD chiller
UR - http://www.scopus.com/inward/record.url?scp=85126617389&partnerID=8YFLogxK
U2 - 10.1016/j.enbuild.2022.112017
DO - 10.1016/j.enbuild.2022.112017
M3 - Journal article
AN - SCOPUS:85126617389
SN - 0378-7788
VL - 262
JO - Energy and Buildings
JF - Energy and Buildings
M1 - 112017
ER -
