Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming

Deng, Kun; Sun, Yu; Li, Sisi; Lu, Yan; Brouwer, Jack; Mehta, Prashant G.; Zhou, MengChu; Chakraborty, Amit

doi:10.1109/tase.2014.2352280

Cited by 94 publications

(25 citation statements)

References 37 publications

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“…Various heuristic and conventional algorithms such as dynamic programming [7,8], genetic algorithm (GA) N [9], multi-phase GA [10], GA in coupling MATLAB and TRNSYS software [11], firefly algorithm [12], particle swarm optimization [13,14], cuckoo search [15], evolution strategy [16], simulated annealing [17], differential evolution [18], equal loading rate [19], neural network [20] and GAMS [21] have been applied to optimize the chiller loading problem.…”

Section: Abbreviationsmentioning

confidence: 99%

Risk and Uncertainty Analysis of Cooling Demand in Multi-Chiller System Using Downside Risk Constraints Method

Nojavan

Jermsittiparsert

2020

IEEE Access

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Section: Abbreviationsmentioning

confidence: 99%

Risk and Uncertainty Analysis of Cooling Demand in Multi-Chiller System Using Downside Risk Constraints Method

Nojavan

Jermsittiparsert

2020

IEEE Access

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“…The flow rate of CHW and its return/supply temperatures are two variables that will be affected by any increase/decrease in cooling load consumption [21,[39][40][41][42]. However, energy efficiency will be affected by the increasing/decreasing cooling water return temperature, while reducing this temperature will result in an improved COP [43][44][45][46].…”

Section: Data Modeling Preprocessingmentioning

confidence: 99%

An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus

et al. 2018

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Abstract:Optimal operation is an important aspect of energy efficiency that can be employed to reduce power consumption. In cooling systems, the chillers consume a large amount of electricity, especially if they are not optimally operated, therefore, they cannot produce the required or rated cooling load capacity. The objective of this paper is to improve coefficient of performance (COP) for the operation of chillers and to reduce power consumption. Two contributions in this work are: (1) the prediction of a model by using Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Fuzzy Clustering Subtractive (FCS), and (2) the classification and optimization of the predicted models by using an Accelerated Particle Swarm Optimization (APSO) algorithm. Particularly, in contribution (1), two models are developed to predict/assess power consumption and cooling load capacity. While in contribution (2), the predictive model's data obtained are used to classify the operating performance of the chiller and to optimize the model in order to reduce power consumption and cooling capacity. Therefore, data classification by APSO is used to enhance the coefficient of performance (COP). The proposed technique reduces the total power consumption by 33.2% and meets the cooling demand requirements. Also, it improves the cooling performance based on COP, thus resulting in a 15.95% increase in efficiency compared to the existing cooling system. The studied ANFIS-based FCS outperforms the ANFIS-based fuzzy C-means clustering in terms of the regression. Then, the algorithm-based classifier APSO has better results compared to the conventional particle swarm optimization (PSO). The data was acquired from the District Cooling System (DCS) at the Universiti Teknologi Petronas (UTP) campus in Malaysia.

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“…The academic literature provides rich insights into energy optimization algorithms. Deng et al [46] proposed a dynamic programming and mixed-integer linear programming solution to control a chiller schedule. They modeled the problem with a predictive control problem.…”

Section: Related Workmentioning

confidence: 99%

An ANN-GA Semantic Rule-Based System to Reduce the Gap Between Predicted and Actual Energy Consumption in Buildings

Yüce

Rezgui

2017

IEEE Trans. Automat. Sci. Eng.

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This paper addresses the endemic problem of the gap between predicted and actual energy performance in public buildings. A system engineering approach is used to characterize energy performance factoring in building intrinsic properties, occupancy patterns, environmental conditions, as well as available control variables and their respective ranges. Due to the lack of historical data, a theoretical simulation model is considered. A semantic mapping process is proposed using principle component analysis (PCA) and multi regression analysis (MRA) to determine the governing (i.e., most sensitive) variables to reduce the energy gap with a (near) real-time capability. Further, an artificial neural network (ANN) is developed to learn the patterns of this semantic mapping, and is used as the cost function of a genetic algorithm (GA)-based optimization tool to generate optimized energy saving rules factoring in multiple objectives and constraints. Finally, a novel rule evaluation process is developed to evaluate the generated energy saving rules, their boundaries, and underpinning variables. The proposed solution has been tested on both a simulation platform and a pilot building-a care home in the Netherlands. Validation results suggest an average 25% energy reduction while meeting occupants' comfort conditions. Note to Practioners-This study presents a novel semantic rule generation process using GA and ANN with the objective to reduce the gap between predicted and actual energy consumption in public buildings. Due to the absence of historical energy consumption data, a theoretical simulation approach is used that takes into account a wide range of factors, including building fabric, occupancy patterns, and environmental conditions. Energy sensitive variables are then identified using PCA and MRA. These sensitive variables as well as available control variables (set points) are used to train an ANN to learn energy consumption patterns and behavior within the considered buildings. This trained network is then used as a cost function engine (predictor) for a GA-based optimization process to generate the optimized energy saving rules. Finally, a novel rule evaluation process is devised and implemented to assess energy saving rules quality and boundaries. All generated rules have been tested on both the proposed simulation environment and real buildings. Validation results suggest an average 25% energy reduction.

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Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming

Cited by 94 publications

References 37 publications

Risk and Uncertainty Analysis of Cooling Demand in Multi-Chiller System Using Downside Risk Constraints Method

Risk and Uncertainty Analysis of Cooling Demand in Multi-Chiller System Using Downside Risk Constraints Method

An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus

An ANN-GA Semantic Rule-Based System to Reduce the Gap Between Predicted and Actual Energy Consumption in Buildings

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