**Published at : ** 17 May 2024

**Volume :** **IJtech**
Vol 15, No 3 (2024)

**DOI :** https://doi.org/10.14716/ijtech.v15i3.6728

Dapito, J.L., Chua, A.Y., 2024. Coefficient of Performance Prediction Model for an On-Site Vapor Compression Refrigeration System Using Artificial Neural Network.

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Jeffrey L. Dapito | Department of Mechanical Engineering, Gokongwei College of Engineering, De La Salle University Manila, Taft Avenue, Manila City, Metro Manila, 0922 Philippines |

Alvin Y. Chua | Department of Mechanical Engineering, Gokongwei College of Engineering, De La Salle University Manila, Taft Avenue, Manila City, Metro Manila, 0922 Philippines |

Abstract

Refrigeration
system is essential
in ensuring the comfort of people,
preserving food for extended periods, and supporting the functionality of
technological devices.
However, refrigeration system accounts for
approximately 17% of global electricity consumption due to the
substantial energy requirement of compression work. This high consumption rate shows
the need to reduce operational and maintenance costs by monitoring the efficiency of refrigeration system using Coefficient of Performance (COP). Currently, there are two methods of monitoring
COP, namely substituting actual values into theoretical formulas, and developing
artificial intelligence model for COP values. Therefore, this study aimed to develop COP prediction model using Artificial Neural
Network (ANN) at a room set point temperature of -25°C. The results showed that through the analysis of ANN
parameters, prediction model was successfully developed with an RMSE of 0.0621, an R^{2} value of 0.8162,
and a training speed of 27.3 seconds. The developed
prediction model had a CvRMSE value of 3.41 and an MBe of 0.14 which falls within the
acceptable values. The prediction model was able to predict COP values of other
CDUs, with the same specification, for set point temperature of -21°C. This study showed a promising strategy for monitoring COP of an on-site vapor compression
refrigeration system using a data-driven method.

Artificial Neural Network; Coefficient of Performance; Machine Learning; Refrigeration; Vapor compression system

Introduction

Refrigeration system is
essential in modern lives, ensuring comfort, preserving food for
extended periods, and supporting the functionality of technological devices (Poggi *et al*.,
2020). Whether for air
conditioning, heat pumps, or
refrigeration purposes, all these applications require a vapor compression
system, accounting for
approximately 17% of global electricity consumption due to the substantial
energy requirement of compression work (Ustaoglu *et al*., 2020).
In the Philippines, there is a
projected doubling of overall energy demand by the year 2035 compared to the 2010 levels,
with an average annual increase of 2.9%. Moreover,
the Energy Policy and Planning Bureau of the Department of
Energy has prioritized
energy efficiency as a fundamental aspect of the Filipino way of life,
targeting a 10% reduction in energy consumption by 2030 (Martinez, 2017).

The Energy
Efficiency and Conservation Act,
also known as the Republic Act 11285 of 2019, has established energy efficiency and
conservation as a legal
obligation to people. This
law unites various
sectors to make energy efficiency and conservation a way of life for Filipinos.
Apart from establishing an
inter-agency energy efficiency and conservation committee as well as implementing a certification system for Energy
Conservation Officers (CECO) and Energy Managers (CEM), this act also directs
establishments to integrate Energy Management Systems into the operations,
promoting strategic measures
and initiatives.

The development and implementation of monitoring
system as well as integration of predictive model are essential steps to reduce
energy consumption and predict potential failure of a system (Noorsaman *et
al*., 2023; Sari* et al*., 2023; Louhichi, Sallak, and Pelletan,* *2022;
Katipamula and Brambley, 2005). To
ensure the efficient operation of cooling system, there is a need to implement energy efficiency
measures to effectively reduce electricity consumption while sustaining the
desired temperature for the cooled area (Ahmed *et al.,* 2021;
Olughu, 2021).
This phenomenon can be achieved by
monitoring and analyzing Coefficient of Performance (COP) of system, as a common method
of determining the efficiency of cooling applications. Several studies conducted
by Yu and Chan underscore
the importance of monitoring COP in decreasing the electricity consumption of cooling system. These studies analyzed changes in
COP under different operating conditions,
investigating whether low values can be improved
through design and operational control adjustment. For
example, COP was
increased from 11.4% to
237.2% by identifying the optimum set point for condensing temperature, resulting in an electricity consumption reduction of approximately 14.1 kWh/m^{2} of area in
an HVAC application. These results showed that
simulation studies could offer valuable insights for HVAC
engineers to optimize
the operation of
cooling system (Yu and Chan, 2008; Yu, Chan,
and Chu, 2006; Yu and Chan, 2005).

Monitoring COP is crucial for
developing predictive approaches to conduct necessary maintenance activities. For instance,
a study by Yu,
Chen, and Chen
(2020) explored the application
of the Internet of Things (IoT) in COP forecasting using a Long Short-Term
Memory (LSTM) network, a deep learning model, in the context of predictive
maintenance (Yu,
Chen, and Chen,
2020). This
study showed that a good
COP prediction could facilitate timely maintenance of
cooling system to reduce unnecessary energy and cost losses.
Breuker from the ASHRAE journal has also showed the
impact of machine fault on COP (Breuker, Rossi,
and Braun, 2000). Through performance indices, it was
observed that refrigerant leakage reaching
a fault level of 14% could lead to 4.6%
reduction in COP. Additionally,
condenser fouling was found to have a greater
effect on COP compared to
capacity, potentially indicating a
looming failure. Consequently, as equipment function decreases severely, the need for maintenance activity increases significantly.

A Systematic Literature Review
(SLR) conducted by Dalzochio explored the challenges and application of Machine
Learning (ML) in predictive maintenance in the context of Industry 4.0 (Dalzochio *et al*.,
2020). Obtaining early
insight into the physical
system using predictive model offers various
benefits including productivity
improvement, reduction of system faults, minimization of unplanned downtimes,
increased efficiency in financial and human resources, as well as optimization in planning the maintenance interventions (Nacchia *et al*.,
2021). The study also
explored predictive maintenance challenges in the business context, emphasizing the importance for companies
to adopt predictive model capable of providing early indications of potential
anomalies according to the
impact of failure on the plant operations. Despite
the advantages, some companies opt for redundant equipment
that can take over when the primary
hardware fails, rather than
investing in predictive maintenance
approach.

For the development of COP prediction model for cooling or heating system,
several studies have been carried out using
different types of ML. Through statistical analysis of
parameters, ANN-based COP prediction model was developed for a heat pump. The results showed that ANN produced the lowest Mean
Bias Error (MBE) of -3.6 compared to the Support Vector Machine (SVM), Random
Forest, and K-nearest neighbor. Due to
the superior accuracy and fast calculation time, the trained model was applied to a Building Automation System (BAS) to
monitor system performance in real-time (Shin and Cho, 2021).
Similarly, Tian developed COP prediction
model for an on-site screw compressor cooling
system in a cinema setup (Tian *et al*., 2019).
The result showed that ANN produced a maximum error of 5.8% and the prediction
from experimental data ranged between
positive or negative 5%, indicating the significance of ANN on
water-cooled screw cooling system.

Several studies have applied
ML to predict COP in energy efficiency
and predictive maintenance contexts. In this study,
ANN is proposed for predicting COP of an on-site refrigeration system as a
promising approach to address the challenges of monitoring COP of decentralized
industrial cooling system. Specifically,
the objectives of this study include developing
a ML-based COP
prediction model for a vapor compression refrigeration system using ANN. The experiment was carried out to manually
determine the effectiveness and limitations of model in predicting COP for other decentralized condensing units with similar specifications. The input variables used
were selected using statistical analysis, with
COP theoretically measured
using evaporation temperature and
pressure, as well as condensing
and subcooled temperature. In conclusion,
ANN showed a high accuracy and fast training
time, indicating the effectiveness in predicting COP of an on-site vapor compression refrigeration system.

Experimental Methods

*2**.**1**. **System description*

The equipment used is in a decentralized
cold storage facility in Paranaque City, Philippines. Specifically, the
equipment has been operating for 4 years and consists of a Mitsubishi
ECOV-EN270VC1 brand of Condensing Unit (CDU), with a scroll inverter type
compressor that uses R410a refrigerant. Furthermore, it is known for energy
saving, high efficiency, and compactness, which is typically used in air
conditioning or refrigeration applications (Wang *et al*., 2021). In this study, the equipment is set to maintain a room temperature of
-25°C. The cooling unit is installed with a water defrost system, which is
scheduled every 2:00 AM and 2:00 PM. The system includes an inverter scroll
compressor, an evaporator, an expansion valve, and an air-cooled condenser.

*2**.2. **Data collection*

Currently, there are two major methods
used in the industry for monitoring COP. The first is by fitting the actual
refrigeration parameters to a theoretical formula, while the second method
entails training artificial intelligence model to predict future values (Tian *et al*.,
2019). In this study, the second method
was used, proposing the use of ANN model trained on data from an on-site
refrigeration system. The model is expected to predict COP of other condensing
units with similar model and specification. Specifically, 3770 data points for
each refrigeration parameter were used, with 70% allocated for training Machine
learning (ML) model while the 30% remaining were applied for testing. The
equipment was located on the machine deck above the cold room facility on an
outdoor setup. Data were extracted through XWEB Evo at an interval of 5 minutes
for the entire month of June. Subsequently, to capture the system COP, a
theoretical method was used. By applying parameters such as evaporating
temperature, suction pressure, condensing temperature, and subcooled temperature, the enthalpies of refrigerant were
interpolated from the saturated and superheat property table of R410A
refrigerant to calculate COP using the formula from the ASHRAE (2013) Handbook: Fundamentals. The following formula was used to obtain the
value of COP (see Equation 1).

COP of vapor compression system serves as a measure of compressor
efficiency. Furthermore, it is the ratio of cooling effect achieved by system
to energy supply under certain conditions (Jani *et al.,* 2017; Jani
*et al.,* 2016). Equation 1, shows the
theoretical formula for obtaining COP of the system. Where COP represents the
coefficient of performance of the system, Q_{in} indicates the heat
absorption in evaporator, Q_{out} is the heat rejection in the
condenser, W_{comp} is the work input from the compressor, and h_{1}
to h_{4} [kJ / kg – K] denotes the enthalpy in the four stages of the
vapor compression cycle.

*2**.3. **Input value selection*

An effective ML prediction model depends on the quality and significance of input data
used in training (Fenza *et al*., 2021).
Therefore, statistical analysis was used to identify refrigeration parameters
with the most significant relationship to COP. This could help reduce the
amount of data to be collected by extracting relevant information (Jittawiriyanukoon
and Srisarkun, 2018). In this context, COP is the
dependent variable while suction pressure, suction temperature, evaporation
temperature, condensing temperature, condensing pressure, EXV opening,
subcooled temperature, and superheat are independent variables. Table 1 shows
the summary of regression analysis and ANOVA conducted on all variables. Among
the input variables, suction pressure and temperature, including evaporation
temperature have the highest R^{2} values. Moreover, R^{2 }shows
the extent to which changes in dependent variables are explained by independent
variables, as values closer to 1 indicate a better explanatory power (Chicco,
Warrens, and
Jurman, 2021). In this case, the
three variables have the highest values and the most significant relationship
with the dependent variable, namely COP. Meanwhile, the p-value is a measure
that gives credibility to statistical analysis, with a lower value indicating a
greater significance in the observed variances (Maheshwarappa and Majumder,
2023).

All input variables, except subcooled temperature, showed a
statistically significant value of below 0.05. Additionally, a separate feature
ranking algorithm was used in MATLAB to identify input variables with the most
significant relationship to the output variable. Based on the results, suction
pressure, suction temperature, and evaporation temperature have the highest
importance scores in the F-test conducted, indicating the most significant
refrigeration parameters regarding COP.

*2**.4. **Prediction model development*

ANN is a ML model
used to determine the significant relationship between input and output
variables. This model is composed of interconnected neurons containing an
activation function with three structural layers, namely input, hidden, and output
(Fagbola,
Thakur, and Olugbara, 2019). In this study,
three fully connected (FC) layers comprising 10 nodes each, excluding the final
fully connected neurons, are used for model training. Neurons are
interconnected with weight reflecting the output of neurons relative to others (Pérez-Gomariz,
López-Gómez, and Cerdán-Cartagena, 2023). Some of
the common activation functions used in ANN are the Sigmoid and the Rectified
Linear Unit (ReLU) to address the “expansion as well as disappearance” problem
usually encountered in sigmoid and tanh functions. The use of the ReLU
introduces sparsity to the computation, improving efficiency regarding time and
space complexity (Bai, 2022). Therefore, the
ReLU activation function was used in this
study with an iteration limit of 1000, training only suction
pressure, suction temperature, and evaporation temperature among the 8 input variables.

*2**.5. **Accuracy Metrics*

The two main parts of developing a prediction model
using ANN are training and testing. Similar to other ML, ANN uses a high percentage of data for training by arriving at an
optimal weight of the network. In this study, 70% of data was used to train the
model while the remaining 30% was used for testing (Genç, and Tunç, 2019). To
analyze the accuracy of prediction model relative to each other, several
metrics were used such as Coefficient of determination (R^{2}) and Root
Mean Squared Error (RMSE). In ML, R^{2}
is a metric that shows how effectively model explains the fitted data in the
regression model, with higher values representing better explanatory power.
Meanwhile, RMSE shows a clear view of the model performance by indicating the
dispersion degree of the data. The lower the RMSE value the better the ML model and its prediction (Tyagi *et al.,* 2022). Both metrics can be calculated using the formula shown below, as
expressed in Equations 2 and 3 (Tian *et al*., 2019).

In the
equations, n represents the sample size, y_{i} is the actual value, and
is the
predicted value. In addition to the metrics, this study used the coefficient of variation for root mean squared error
(CvRMSE) and Mean Bias Error (MBE) to evaluate the application of the trained
prediction model. ASHRAE guideline 14 provided a standard of 10% for MBE and
less than 30% for CvRMSE (Shin and Cho, 2021). Equations 4 and 5 are the formulas for CvRMSE and MBE.

*2**.6. **Comparison of the prediction model*

Currently, there are several regression ML model that can be used to predict desired parameters such as Regression
trees, Support Vector Machine (SVM), Gaussian Process Regression, and kernel
approximation regression (Sarker, 2021). An
analysis using MATLAB, compared the effectiveness of ANN to other ML models. Table 2 shows the comparison of accuracy metrics and learning
speed between different models.

**Table 2** Comparison of different
regression models’ metrics and learning speed

In this study, the Gaussian process regression used for analysis is a
matern 5/2 kernel function, with an isotropic kernel and a constant function.
The regression tree is a course tree with a minimum leaf size of 36, while the
support vector machine model is a medium Gaussian SVM with a kernel scale of
1.7. Additionally, the last model is SVM Kernel with an iteration limit of
1000. Although ANN and Gaussian process regression have the
same significance in terms of accuracy, there
is a difference in terms of
learning speed and training time. Approximately, 69,100 observations
per second and a difference of
172.48 seconds are identified between the two
models. Based on the results,
the lowest RMSE value and highest observation rate of ANN show a better prediction model compared to others.

*2**.7. **Optimization, validation, and testing*

In addition to the feature selection conducted, ANN model was analyzed
using different numbers of nodes and types of activation functions. Table 3
shows that RMSE and R^{2} value changes based on variation in the
number of hidden nodes. The results showed that RMS error was smallest when
there were 10 hidden nodes under the ReLU function, leading to an RMSE of
0.0621 and an R^{2} value of 0.8162. Therefore, a hidden layer node of
10 was used in training ANN prediction model.

*2**.8. **Automation Concept*

Apart from
accuracy, ANN is also capable of a higher learning rate compared to other ML
models (Shin
and Cho, 2021). Figure 1 shows the complete process flow in developing,
testing, and applying ANN-based COP prediction model used in this study. The
first part includes the development of prediction model by training 70% of the
data and testing 30%. The second part includes an automation concept for the
deployment of prediction model. Using refrigeration
data at a normal operating condition,
this study also proposes a future improvement integrating a fault detection and
diagnosis ML model. Moreover, several
studies, including Han *et al.* (2010)
have developed an automated Principal
Component Analysis (PCA) – Support Vector Machine (SVM) based AFDD of a vapor
compression refrigeration system. (Han *et al*., 2010).

Results and Discussion

*3.1. **Performance
of prediction model*

Figure 2 shows the graph of the actual COP value relative to the predicted response. The straight diagonal line shows the perfect prediction, with closer values representing better results. The graph shows that the predicted values are close to the perfect prediction line. This indicates that the trained ANN prediction model can predict the test data with a small amount of error.

**Figure
2** True and predicted result (a) and
comparison of predicted values to actual (b)

The second graph shows the predicted and actual COP against each data point used during model testing. The slight disparity in RMSE, MSE, CvRMSE, and MBE along with the positive graph of the predicted and actual COP, shows the effectiveness of ANN model developed. Table 4 also depicts the accuracy metrics of the trained ANN prediction model, with a detailed comparison between the different accuracy metrics conducted for the two tests. Compared to standard set by ASHRAE (2014) guideline 14, the table shows that CvRMSE of 3.41 is below 30%, while the MBE of 0.18% does not meet the threshold of 10%. These results show the effectiveness of the prediction model.

Figure 3 shows a partial dependence plot and the relationship between each variable in the COP to understand the model and its three input variables. Evaporation temperature and suction pipe temperature show similar characteristics that increase as COP decreases. However, suction pressure increases as the COP of the system rises.

**Figure 3**
Partial dependence plot of evaporation temperature (a), suction pipe
temperature (b), and suction pressure (c), respectively

*3**.2. Prediction model application*

An
additional test was conducted using input data from two different condensing
unit (CDU) of the same model and brand. CDU 1 was set at -18°C cooled
temperature while CDU 2 was set at -21°C temperature. CDU 2 showed better prediction results compared to CDU 1 in terms of
RMSE with a value
lower than the training metrics. For the R^{2} value, CDU 1 has a
significantly low value of -0.4 compared
to the R^{2} value of 0.75 of CDU 2. The CvRMSE for
both CDUs falls within the standard set by ASHRAE (2014) guideline
14, which is below 30%. Meanwhile, the MBE for CDU1 falls slightly below the standard of 10%,
which is contrary to the MBE of CDU2. With
different set point temperatures, the results show that room temperature and equipment condition are
factors in the prediction capability of model. This is
attributed to the compressor functioning at different pressures
based on the operating condition. Figure 4 shows the comparison
chart of the actual and predicted values for CDU 1 and CDU 2, respectively.

**Figure
4** Comparison between predicted and
actual COP of CDU 1 (a) and CDU 2 (b)

Conclusion

In conclusion, this study
successfully developed a machine learning-based COP
prediction model using the actual operating data from an on-site scroll-type
compressor refrigeration system with a
vapor compression refrigeration cycle. A total
of 3770 data for each parameter was used, where 70% was allocated for
prediction model training and 30%
for testing. Statistical
analysis was conducted on the 8
refrigeration parameters to improve the
result. Among these parameters, suction
temperature, suction pressure, and evaporation temperature with the highest RMSE, MSE, and R^{2} values were
selected as input variables for training
ANN model. By comparing the trained ANN model at -25C set point temperature to
other ML algorithms, ANN has the highest RMSE value of 0.06, MSE of 0.004, and
R^{2} of 0.82, indicating a
good prediction capability of COP. Through
the change of nodes number
and activation function type, the result showed
that the ReLU
function with 10 nodes for each hidden layer produced the lowest RMSE value. Furthermore, the prediction model developed was
tested using data from another condensing unit (CDU) with the same model, showing
the potential to predict
COP at approximately
-21 set point temperature. This study
showed a promising strategy for monitoring COP of decentralized
refrigeration system using a data-driven method.

Acknowledgement

The author is grateful to the Department of Mechanical Engineering at De
La Salle University for the support provided and to Glacier Megafridge Inc. for
sharing their data.

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