Optimal chiller loading by improved sparrow search algorithm for saving energy consumption

Xue, Zhilu; Yu, Junqi; Zhao, Anjun; Zong, Yue; Yang, Siyuan; Wang, Meng

doi:10.1016/j.jobe.2023.105980

Cited by 17 publications

(6 citation statements)

References 29 publications

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“…SSA is a novel swarm intelligence optimization algorithm with the advantages of simple implementation, easy expansion, and self-organization, 44 and has been widely used. [45][46][47][48] SSA obtains optimal solutions by simulating sparrow population intelligence and foraging behavior. The sparrow foraging process can be idealized as a producer-scrounger model.…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

Action reliability analysis under random‐interval hybrid uncertainty of chain conveyor based on adaptive intelligent extremum surrogate model

Wen,

Hou,

Lin

et al. 2023

Quality & Reliability Eng

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The action responses of controlled mechanisms are often multiparametric, nonlinear, and uncertain. Complex dynamics and limited uncertain information pose difficulties for action reliability analysis. This paper develops an adaptive intelligent extremum surrogate model (AIESM) method for the action reliability under random‐interval hybrid uncertainty of a chain conveyor. First, a dynamic model of the chain conveyor is established, which considers the impact and frictional effects within the mechanical system, the regulating effects of the control system, and the external disturbances of the system. After that, a hybrid kernel extreme learning machine optimized by the sparrow search algorithm is employed as an intelligent surrogate model to construct the initial surrogate model from the hybrid uncertain variables to the limit state function (LSF) response and the extremum surrogate model (ESM) from the random variables to the LSF extremum response. An adaptive infilling strategy combining active learning and opposition‐based learning is applied to improve the accuracy and efficiency of the ESM and reduce the estimation error of action reliability. Finally, the action reliability interval bounds are obtained by Monte Carlo simulation based on the ESM. Two numerical examples are presented to illustrate the validity of the AIESM method. The action reliability interval of the chain conveyor provided by the proposed method is [0.9706, 0.9923].

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Section: Sparrow Search Algorithmmentioning

confidence: 99%

Action reliability analysis under random‐interval hybrid uncertainty of chain conveyor based on adaptive intelligent extremum surrogate model

Wen,

Hou,

Lin

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…In the two cases, the results of MSSA were compared with GA [ 42 ], PSO [ 45 ], SSA [ 60 ], ARO [ 59 ] and HHO [ 58 ], which are represented in Table 8 and Table 9 , severally.…”

Section: Application Of Mssamentioning

confidence: 99%

“… Load GA [ 42 ] PSO [ 45 ] SSA [ 60 ] ARO HHO MSSA Energy saving (kW) Demand P/kW(A) P/kW(B) P/kW(C) P/kW(D) P/kW(E) P/kW(F) (F-A) (F–B) (F–C) (F-D) (F-E) 6858RT 4766.33 4739.78 4768.23 4739.91 4738.58 4738.58 −27.75 −1.20 −29.65 −1.34 0.00 6477RT 4459.16 4423.05 4443.71 4422.30 4421.71 4421.65 −37.51 −1.40 −22.06 −0.66 −0.06 6096RT 4185.87 4147.81 4160.34 4143.89 4143.71 4143.71 −42.16 −4.10 −16.63 −0.18 0.00 5717RT 3940.60 3920.96 3896.26 3843.02 3842.55 3842.55 −98.05 −78.41 …”

Section: Application Of Mssamentioning

confidence: 99%

Energy-saving optimization of the parallel chillers system based on a multi-strategy improved sparrow search algorithm

Shao,

Yu,

et al. 2023

Heliyon

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“…Olszewski [10] ascertained the superior performance of variable speed chillers to handle part-load conditions compared with common on/off controlled chillers. Some recent studies in references [11][12][13][14][15][16][17][18] focus on the optimum loading of multiple chillers with various optimization algorithms. The chiller coefficient of performance (COP)-cooling capacity output in kW over chiller electric power input in kW-was expressed as a function of part-load ratio (PLR), only without considering the temperatures of chilled water and condenser water.…”

Section: Reference Research Problem Variables Optimized Optimization ...mentioning

confidence: 99%

Time Series Forecast of Cooling Demand for Sustainable Chiller System in an Office Building in a Subtropical Climate

2023

Sustainability

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Commercial buildings can take up one-third of the energy related carbon emissions. There is limited research on forecasting cooling demands to evaluate sustainable air conditioning systems under climate change. This paper develops a simplified cooling demand model based on the time series of climatic and architectural variables to analyze carbon reduction by a sustainable chiller system. EnergyPlus is used to simulate hourly cooling demands of a hypothesized high-rise office building in Hong Kong under a change of architectural parameters and future climate conditions. An hourly cooling demand model with R2 above 0.9 is developed with inputs of the window-to-wall ratio, outdoor air enthalpy, global solar radiation, wind speed and their two steps ahead. The validated model is then used to analyze carbon reduction potentials by free cooling and a full variable speed chiller system. The low carbon technologies reduce carbon emissions by over 20% with but the reduction shrinks to 2.51–4.93% under future climate conditions. The novelty of this study is the simplified cooling demand model based on the time series of climatic and architectural variables. The significances of this study are to quantify carbon reduction by a sustainable chiller system under climate change and to appeal for more carbon reduction technologies for carbon neutrality.

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Optimal chiller loading by improved sparrow search algorithm for saving energy consumption

Cited by 17 publications

References 29 publications

Action reliability analysis under random‐interval hybrid uncertainty of chain conveyor based on adaptive intelligent extremum surrogate model

Action reliability analysis under random‐interval hybrid uncertainty of chain conveyor based on adaptive intelligent extremum surrogate model

Energy-saving optimization of the parallel chillers system based on a multi-strategy improved sparrow search algorithm

Time Series Forecast of Cooling Demand for Sustainable Chiller System in an Office Building in a Subtropical Climate

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