Sequencing of chillers by estimating chiller power consumption using artificial neural networks

Chang, Yung-Chung

doi:10.1016/j.buildenv.2005.08.033

Cited by 34 publications

(10 citation statements)

References 9 publications

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“…The final step of LMN is to sum up all local models to obtain an estimated value. The prior set criteria of performance index, e.g., MSE, as shown in (18), is checked to determine whether a new cluster center should be added or not.…”

Section: B Lmn Identification Based On Sfcmmentioning

confidence: 99%

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Electricity Gain via Integrated Operation of Turbine Generator and Cooling Tower Using Local Model Network

Pan

Shieh

Jang

et al. 2011

IEEE Trans. Energy Convers.

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This paper presents a model-based optimization approach to determine operation mode of a set of fans in the cooling tower (CT) by considering turbine generator and CT as an integrated system. The optimization problem is formulated as to maximize the net power output, which is the result of subtracting power consumption of fans in CT from power generated by turbine generator. The operations of both units are characterized by a data-driven method, local model network, which presents a set of local models in the formats of linear equations. Stepwise variable selection is employed to identify key variables as a way of reducing the number of variables and avoiding overfitting. Satisfactory fuzzy c-mean cluster is used to categorize operation data into several groups to build local models. The optimal problem is then solved by linear programming algorithm. A case study conducted in a commercial plant demonstrates that the proposed approach can increase significant net power output.

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Section: B Lmn Identification Based On Sfcmmentioning

confidence: 99%

“…Chang [18] modeled a chiller system with an ANN of 4-20-40-1 structure, i.e., having an input layer of four neurons, two hidden layers of 20 and 40 neurons, and an output layer of one neuron. The acquired model is then used to determine the optimal start-up sequences of six chillers.…”

mentioning

confidence: 99%

Electricity Gain via Integrated Operation of Turbine Generator and Cooling Tower Using Local Model Network

Pan

Shieh

Jang

et al. 2011

IEEE Trans. Energy Convers.

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show abstract

“…The ability to learn is one of the outstanding characteristics on an ANN. ANNs can model multiple parameters simultaneously for nonlinear systems and are now widely used for predictive control, such as solar radiation (Ló pez et al, 2001(Ló pez et al, , 2005Bosch et al, 2008), energy use prediction, energy optimization (Chow et al, 2002), data trending, and optimum start and stop (Wang, 2001;Chang, 2007).…”

Section: Introductionmentioning

confidence: 99%

Performance study of solar-assisted air-conditioning system provided with storage tanks using artificial neural networks

Rosiek

Batlles

2011

International Journal of Refrigeration

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“…The first category includes the methods which have been proposed to improve chiller operation ( [5], [6], [7], [2], [8], [9]). In [5] an artificial neural network is used to obtain the chiller power consumption model which is then used for optimizing the chiller sequence.…”

Section: Introductionmentioning

confidence: 99%

“…In [5] an artificial neural network is used to obtain the chiller power consumption model which is then used for optimizing the chiller sequence. In [6], a Lagrangian method and dynamic programming are adopted to improve the COP of a single chiller plant with multiple chillers.…”

Section: Introductionmentioning

confidence: 99%

Green Scheduling for Energy-Efficient Operation of Multiple Chiller Plants

Behl

Nghiem

Mangharam

2012

2012 IEEE 33rd Real-Time Systems Symposium

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In large building systems, such as a university campus, the air-conditioning systems are commonly served by chiller plants, which contribute a large fraction of the total electricity consumption of the campuses. The power consumption of a chiller is highly affected by its Coefficient of Performance (COP), which is optimal when the chiller is operated at or near full load. For a chiller plant, its overall COP can be optimized by utilizing a Thermal Energy Storage (TES) and switching its operation between COP-optimal charging and discharging modes. However, uncoordinated mode switchings of chiller plants may cause temporally-correlated high electricity demand when multiple plants are charging their TES concurrently. In this paper, a Green Scheduling approach, proposed in our previous work, is used to schedule the chiller plants to reduce their peak aggregate power demand while ensuring safe operation of the TES. We present a scheduling algorithm based on backward reach set computation of the TES dynamics. The proposed algorithm is demonstrated in a numerical simulation in Matlab to be effective for reducing the peak power demand and the overall electricity cost.

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Sequencing of chillers by estimating chiller power consumption using artificial neural networks

Cited by 34 publications

References 9 publications

Electricity Gain via Integrated Operation of Turbine Generator and Cooling Tower Using Local Model Network

Electricity Gain via Integrated Operation of Turbine Generator and Cooling Tower Using Local Model Network

Performance study of solar-assisted air-conditioning system provided with storage tanks using artificial neural networks

Green Scheduling for Energy-Efficient Operation of Multiple Chiller Plants

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