Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

Kim, Jee-Heon; Seong, Nam-Chul; Choi, Wonchang

doi:10.3390/en12152860

Cited by 34 publications

(18 citation statements)

References 16 publications

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“…As the literature shows, predictions that use machine learning algorithms offer advantages in that they do not require complex modeling compared to simulation methods that are based on mathematical models. As shown, researchers have conducted various studies of heating and cooling load prediction methods that employ machine learning algorithms [12][13][14][15][16]. However, in order to improve the accuracy of such predictions, the researchers either combined several neural network models or utilized complex structures, such as deep layers.…”

Section: Introductionmentioning

confidence: 99%

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

2019

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Accurate calculations and predictions of heating and cooling loads in buildings play an important role in the development and implementation of building energy management plans. This study aims to improve the forecasting accuracy of cooling load predictions using an optimized nonlinear autoregressive exogenous (NARX) neural network model. The preprocessing of training data and optimization of parameters were investigated for model optimization. In predictive models of cooling loads, the removal of missing values and the adjustment of structural parameters have been shown to help improve the predictive performance of a neural network model. In this study, preprocessing the training data eliminated missing values for times when the heating, ventilation, and air-conditioning system is not running. Also, the structural and learning parameters were adjusted to optimize the model parameters.

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Section: Introductionmentioning

confidence: 99%

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

2019

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“…P total = 2374.21 from the GA algorithm. Moreover, the propose algorithm is able to give the distribution of P total in Figure 8 for all points satisfying the indoor temperature constraint in (17). In order to observe the interior of Figure 8a, the 3D cloud image in Figure 8a is unfolded along the z axis (R CHW ), and twenty 2D cloud images are obtained as shown in Figure 9.…”

Section: Performance Comparison Of Optimization Algorithmsmentioning

confidence: 99%

“…Zakula et al [16] took the adaptive grid search technique to map the optimal heat pump performance as a function of the capacity, indoor and outdoor temperatures. Kim et al [17] developed an energy performance prediction and optimization software to predict and optimize the energy consumption of a chiller system using a machine learning algorithm based on artificial neural network (ANN) models. Kusiak et al [18] adopted the multi-layer perception (MLP) algorithm to construct energy consumption predictive models of an air handling unit, and the dynamic penalty-based electromagnetism-like algorithm (DPEM) to minimize the overall energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Power Consumption of an Experimental HVAC System Based on Parallel Grid Searching

Xiong

Wang

2020

Energies

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This paper proposes a parallel grid search algorithm to find an optimal operating point for minimizing the power consumption of an experimental heating, ventilating and air conditioning (HVAC) system. First, a multidimensional, nonlinear and non-convex optimization problem subject to constraints is formulated based on a semi-physical model of the experimental HVAC system. Second, the optimization problem is parallelized based on Graphics Processing Units to simultaneously compute optimization loss functions for different solutions in a searching grid, and to find the optimal solution as the one having the minimum loss function. The proposed algorithm has an advantage that the optimal solution is known with evidence as to the best one subject to current resolutions of the searching grid. Experimental studies are provided to support the proposed algorithm.

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“…This research team is developing a centralized air conditioning system and energy management technique for BEMS applications and has researched energy consumption and load predictions based on ANN (Artificial Neural Networks). Using ANN models, the team conducted a prediction study of chiller energy consumption and achieved results that satisfy the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) criteria, with an average CV(RMSE) of 19.49% in the training period and an average CV(RMSE) of 22.83% in the testing period [17]. In addition, the team conducted studies to optimize the cooling load prediction model based on MATLAB's NARX (with eXigenous) Feedforward Neural Networks model to confirm that a forecast accuracy of less than 7% CV(RMSE) can be obtained depending on the conditions [18].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models

2020

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Air conditioning in buildings accounts for 60% of the total energy consumption. Therefore, accurate predictions of energy consumption are needed to properly manage the energy consumption of buildings. For this purpose, many studies have been conducted recently on the prediction of energy consumption of buildings using machine learning techniques. The energy consumption of the air handling unit (AHU) and absorption chiller in an actual building’s air conditioning system is predicted in this paper using prediction models that are based on artificial neural networks (ANNs), which simply and accurately allow us to forecast energy consumption with limited variables. Using these ANN models, the energy usage of the AHU and chiller could be predicted by collecting a month’s worth of driving data during the summer cooling period. After the forecast models had been verified, the AHU prediction model showed performance in the ranges of 13.27% to 15.25% and 19.42% to 19.53% for the training period and testing period, respectively, and the mean bias error (MBE) ranges were 4.03% to 4.97% and 3.48% to 4.39% for the training period and testing period, respectively. The chiller prediction model satisfied the energy consumption forecast performance criteria presented by American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) guideline 14 (the measurement of energy and demand savings), with a performance of 24.64~25.58% and 7.12~29.39% in the training period and testing period, respectively, and MBE ranges of 2.59~3.40% and 1.35~2.87% in the training period and testing period, respectively. When the training period and testing period were combined for the AHU data, the actual energy usage forecast showed a lower error rate range of 0.22% to 1.11% for the training period and 0.17% to 2.44% for the testing period. For the chiller data, the error rate range was 0.22% to 2.12% for the entire training period, but was somewhat higher at 11.67% to 15.18% for the testing period. The study found that, even if the performance criteria were met, high accuracy results were not obtained, which was due to the poor data set quality. Although the forecast model based on artificial neural network can achieve relatively high-accuracy results with sufficient amounts of data, it is believed that this will require a thorough verification of the data used, as well as improvements in the predictive model to avoid overfitting and underfitting, to achieve such good results.

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Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

Cited by 34 publications

References 16 publications

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

Minimizing Power Consumption of an Experimental HVAC System Based on Parallel Grid Searching

Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models

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