Evaluation of machine learning-based applications in forecasting the performance of single effect absorption chiller network

Panahizadeh, Farshad; Hamzehei, Mahdi; Farzaneh–Gord, Mahmood; Villa, Alvaro Antonio Ochoa

doi:10.1016/j.tsep.2021.101087

Cited by 12 publications

(2 citation statements)

References 44 publications

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“…There has been considerable work optimizing HVAC systems using machine learning. In the online setting, Kim et al [2019], Panahizadeh et al [2021], Park et al [2019] have leveraged neural networks to build data driven models of chillers which can be used downstream for optimization problems.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning

Wong¹,

Dutta²,

Voicu³

et al. 2022

Preprint

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Reinforcement learning (RL) techniques have been developed to optimize industrial cooling systems, offering substantial energy savings compared to traditional heuristic policies. A major challenge in industrial control involves learning behaviors that are feasible in the real world due to machinery constraints. For example, certain actions can only be executed every few hours while other actions can be taken more frequently. Without extensive reward engineering and experimentation, an RL agent may not learn realistic operation of machinery.To address this, we use hierarchical reinforcement learning with multiple agents that control subsets of actions according to their operation time scales.Our hierarchical approach achieves energy savings over existing baselines while maintaining constraints such as operating chillers within safe bounds in a simulated HVAC control environment.

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Section: Related Workmentioning

confidence: 99%

Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning

Wong¹,

Dutta²,

Voicu³

et al. 2022

Preprint

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show abstract

“…Panahizadeh et al predicted the performance and coefficient of thermal energy consumption of absorption coolers using three widely used machine learning methods: artificial neural networks, support vector machines, and genetic programming. When the newly estimated formulas were used for the performance coefficients, and thermal energy consumption of each cooler based on genetic programming, the accuracy of the determinants were 0.97093 and 0.95768 [16]. Charron et al proposed machine learning and deep learning models to predict the power consumption of a water-cooled chiller.…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Predicting Energy Consumption of Absorption Heat Pump with Multilayer Shallow Neural Network Training Algorithms

2021

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The performance of various multilayer neural network algorithms to predict the energy consumption of an absorption chiller in an air conditioning system under the same conditions was compared and evaluated in this study. Each prediction model was created using 12 representative multilayer shallow neural network algorithms. As training data, about a month of actual operation data during the heating period was used, and the predictive performance of 12 algorithms according to the training size was evaluated. The prediction results indicate that the error rates using the measured values are 0.09% minimum, 5.76% maximum, and 1.94 standard deviation (SD) for the Levenberg–Marquardt backpropagation model and 0.41% minimum, 5.05% maximum, and 1.68 SD for the Bayesian regularization backpropagation model. The conjugate gradient with Polak–Ribiére updates backpropagation model yielded lower values than the other two models, with 0.31% minimum, 5.73% maximum, and 1.76 SD. Based on the results for the predictive performance evaluation index, CvRMSE, all other models (conjugate gradient with Fletcher–Reeves updates backpropagation, one-step secant backpropagation, gradient descent with momentum and adaptive learning rate backpropagation, gradient descent with momentum backpropagation) except for the gradient descent backpropagation model yielded results that satisfy ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) Guideline 14. The results of this study confirm that the prediction performance may differ for each multilayer neural network training algorithm. Therefore, selecting the appropriate model to fit the characteristics of a specific project is essential.

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