PREDICTIVE ANALYTIC MODEL
FOR ELECTRICAL CHILLER SYSTEM (ECC)
M.H.M. Nazmi, M. Masdi
Department of Mechanical Engineering, Universiti Teknologi PETRONAS, Malaysia Email: [email protected]
ABSTRACT
District cooling plant is a very complex system consisting of various equipment. One of them is an electric centrifugal chiller that is widely used in the industry to supply chilled water to thermal energy storage (TES). This paper investigates the predictive analytics model of the electric chiller system using real operation data. Data pre-processing was performed to remove the outliers. Further to that, the machine learning model was used to develop an artificial neural network (ANN), whereby the best model with the lowest RMSE was obtained. Then, the ANN was used to correlate between the input and output variables to find the critical parameters which contributed to the Coefficient of Performance (COP).
This model could also predict the output from the actual data of the COP. Lastly, the Shewhart control chart was utilised in the root cause analysis model (RCA) to detect the anomalies based on five critical parameters at the early stage before its failure. The supervised learning algorithms using the feedforward ANN model demonstrates the most accurate predictions compare to all models.
Keywords: chiller, MATLAB, RMSE, Artificial Neural Network, Root Cause Analysis Model, Shewhart Control Chart
INTRODUCTION
The advent of modern technology shows that the Electric Centrifugal Chiller (ECC) has become a favourable system for cooling purposes as compared to the absorption chiller due to higher energy efficiency, higher Coefficient of Performance (COP) and lower CO2 emission than the absorption chiller system [1]. In addition, the electric chiller is widely installed at huge district cooling plants in Malaysia. For example, at Kuala Lumpur City Center, Kuala Lumpur International Airport (KLIA), Putrajaya district as well as in Universiti Teknologi Petronas (UTP) [2]. This work is aimed to develop an artificial intelligence system for the electric chiller model to alarm the machine when ECC performance is degrading. The sub-objectives of this work are to analyse the crucial parameters based on COP and to build machine learning to find out the predicted output using the trained network.
Following that, this would enable the creation of the control limits for the critical parameters in finding out anomalies based on root cause analysis model.
The latest research used Plant Manager Software (PMS) to diagnose data-driven analysis to enhance chiller plants [3]. However, this research had a limitation whereby the result based on the operation data of the chiller needed an iterative application.
Another research was completed using the Simulink model to simulate the chiller systems [4]. Their work demonstrated the methods to acquire accurate calibrated chiller models from accessible data. However, this work showed only the results of using an acritical parameter instead of using various parameters of the electric chiller. An earlier work studied the bottleneck diagnosis approach to electric chiller’s performance at UTP’s district cooling plant [5]. This approach only designed two bottleneck models for ECC’s COP and
Chilled Water supply (CHW) performance analysis.
Thus, there is still a limited artificial intelligence approach being used to investigate the performance of electric chiller, which can connect all the parameters into a model.
Once a building runs the operation of the electric chiller system, significant loss and sometimes affects the overall cooling system may occur if there is a lack of maintenance or repair [6]. Many aspects can result in defects and failures; for example, increment in the flow rate of chilled water by supplying more cooling water. The cooling water will not increase even if the flow rate is increased and this will result in lower operating efficiency of chiller and higher the level of erosion in the chiller’s tube due to tube failure at the early stage. Therefore, it is essential to develop a machine learning model for the electric chiller to observe many parameters and detect significant anomalies before premature failure occurs.
ECC is operated under a complicated cooling system with many types of equipment. However, ECC’s performance will deteriorate after many years of operating, despite constant maintenance. Thus, it is quite difficult for the plant operator to figure out the anomalies, which can influence the ECC performance because of many possible variable parameters.
Furthermore, it is a big challenge to determine the critical parameters that give impact to COP and consequently enable the prediction of the output.
Therefore, artificial intelligence has been developed using a machine learning system to identify the anomalies using the data that periodically monitor the real-time to prevent the ECC’s performance degradation.
The objective of this study is to investigate the predictive analysis model of electric chiller systems using real operation data. The study is aimed to manage the real-time data-based plant that periodically monitors the real electrical chiller plant located at Gas District Cooling (GDC) in Putrajaya. The chiller plant capacity is 12, 000 RT and Thermal Energy Storage (TES) tank capacity is 32,700 RT. This plant has four ECC with integrated chilled water storage system
with 1250 RT for each ECC. Two ECC will directly supply chiller water to the facilities while the other two will act as standby. This study only focuses on one standby ECC for steady-state operation, as shown in Figures 1 and 2. Furthermore, the artificial neural network (ANN) is the preferred method to perform this project and validate the electric chiller model.
An EEC implements the vapour compression cycle to reject the heat obtained from chilled water, as shown in Figure 2. It consists of four major components which are compressor, evaporator, expansion valve and condenser [1]. The superheated refrigerant vapour is transferred to the compressor where the water cools it. At the same time, a cooling tower cooling the water loop when it receives the heat from the compressor.
At high pressure, the vapour is condensed into refrigerant liquid in the condenser. After that, the refrigerant is transferred to the expansion valve to lower the pressure. As the refrigerant reaches low pressure, it is transferred to the evaporator, causing it to boil and change into a vapour. This is because the boiling point of the refrigerant fluid is lower than the temperature of chilled water surrounding the heat source. Lastly, the low pressure of superheated vapour is transferred back to the compressor and the cycle repeats. It is advisable to study the parameter involved such as chiller water entering and leaving temperature, chiller water pressure and water flow in achieving a better COP in an electric chiller [7].
For machine learning, two primary classifications in predictive analytics modelling are machine learning and regression [8]. This project focuses mainly on machine learning technique which is the artificial intelligence that utilises computation algorithms to process the data and generate the best decision with minimal intervention when the new data with the same pattern is received. Thus, machine learning can study from the data and capability learning from the pattern of data given. This shows that machine learning is applicable and relevant to be used in data mining with a large database, to optimise a performance criterion based on the data collected and quality control [8]. Hence, machine learning is the best technique in predictive analytics modelling
of the electrical chiller system. Machine learning can be categorised into supervised learning algorithms and unsupervised learning algorithms. Each of these categories represents different types of data such as a label or unlabelled data as well as different techniques applied.
In supervised learning algorithms, the training or historical data has labelled and correct information or output, related to the iterative process of learning.
The aim is to modify the training data to fit in the predictive model. The model should be trained to reduce total error and achieve a required target
Figure 1 Overview of GDC Putrajaya Plant 1. Figure courtesy of Gas District Cooling (M) Sdn Bhd
Figure 2 Overview of ECC – 0120. Figure courtesy of Gas District Cooling (M) Sdn Bhd
to deliver the predictive output or result [9]. For example, in this project, the predictive machine learning model of electrical chiller was developed to detect the anomaly. This will train the model. The supervised learning algorithms can be evaluated in many ways, such as regression, classification, decision tree, random forest, ANN and k-nearest neighbour (KNN).
execute the algorithms and produce the results for intermediary or hidden layers. The hidden layers can do the mathematical calculations on the inputs. As a result, the output layers linked with the hidden layers to display the results. ANN is capable of performing with vast amounts of data and very effective in predicting future results from the historical data. In addition, ANN can handle inconsistent or incomplete
Figure 3 Neural network with one hidden layer
Figure 4 Neural network with two hidden layers
ANN also known as deep learning, is one of the familiar methods in supervised learning algorithms and is vastly applied to detect building energy prediction.
ANN can simulate like the action of neurons in the brain which capability is to learn from the data. ANN consists of a combination at multiple nodes which can coordinate into layers from interconnected nodes. These nodes will get the input data to
data. Therefore, ANN used in this project to deal with various types of data and perform the predictions analytics model. Figure 3 shows a basic model of a neural network, while Figure 4 shows a more complex neural network.
In data mining, anomaly detection is very significant in determining the condition of the machine. Past
research implemented anomaly detection for detecting anomalous graphs by establishing universal graph coding algorithms using the description length [10]. It generates an unlabelled graphs model with a lossless source coder and the resulting codelength.
When the number of bits required to change, it will deviate the degree distribution as there is an anomalous graph. The advantage of the anomaly detection tool is that it is capable of figuring out the outliers which degrade the predictive model.
Therefore, the anomaly detection model is necessary for predictive maintenance, where the anomalous point can be eliminated. Anomaly detection model has many methods such as extreme value analysis, statistical modelling, proximity-based model and machine learning-based methods. Therefore, the root cause analysis model was applied in this study to identify the anomaly in every five consecutive abnormal data.
Root mean square error (RMSE) is known as the quadratic mean and commonly applied to measure the performance of the model output compared to actual output for continuous target [11]. RMSE also gives rank to the performance of many models on a prediction problem. The smaller the value of RMSE, the better performance and accuracy of the model.
RMSE is the simplest way and good estimator to measure the imperfection of the prediction models with real data:
(1)
Parameter selections is a vital step to be performed when there are various parameters in developing the machine learning model. If a wrong parameter is chosen, it will degrade the prediction model performance and the noise in the modelling.
Researchers raise an issue in evaluating the criticality of parameters for electrical chiller because there are no constant and accurate criteria in selecting the optimum parameters. Thus, the parameters are non- linearly correlated.
The data collection usually rapidly raises until it turns out to be more challenging. Thus, the
dimensionality reduction technique is required to reduce the redundancy in the data and helps to visualise the optimum parameter that can be used in the predictive model [7]. There are many techniques in dimensionality reduction, such as missing value ratio, low variance filter, high correlation filter and random forest, among many others. In this project, the dimensionality reduction technique focusses on Principle Component Analysis (PCA). This technique assists in obtaining a new set of parameters called Principle Components from an existing huge set of parameters available. This principle component is a linear combination of the actual parameter.
In data pre-processing and root cause analysis, it is important to implement the control limits to build up the machine learning modelling. Control limits manage the operation data that goes over the designed limit in each critical parameter and can detect the anomalies to find out RCA. Control limits are different from the specification limits where control limits depend on process variation while specifications limits depend on customer requirements. A few methods have been found that can be applied for the control limits operation [12].
There are two types of outliers, univariate and multivariate [13]. Univariate outliers are when the distribution of the values in a single feature space while multivariate outliers are values in an n-dimensional space like n-features. The data in this project is defined as the univariate outlier because there are extreme values on one variable. Sometimes, it is very difficult for the human brain to find outliers in the distribution of values. Therefore, an outlier detection method is needed to remove the outliers during data cleanings such as Inter Quartile Range (IQR), Tukey’s Method, Z-score, Linear Regression Models, Isolation Forest and others. Tukey’s method is chosen to remove the outliers. The first quartile (Q1) is the 25th percentile of the data, and the third quartile (Q3) is the 75th percentile of the data, while the interquartile range (IQR) is the difference between first and third quartile.
The operation data which falls out of the inner fence can be considered as probable outlier. Furthermore, the operation data is called problematic outlier or
‘far out’ data when it falls out of the outer fence. The formula of the Tukey Method used is given by,
Interquartile Range =
Third Quartile (Q3) – First Quartile (Q1) (2) Inner Fence: [Q1 − 1.5 x IQR, Q3 + 1.5 x IQR] (3) Outer Fence: [Q1 − 3 x IQR, Q3 + 3 x IQR] (4) This method is applied to detect anomalies in the data when it exceeds the lower or upper limits. The Shewhart control chart has upper and lower control limits, which are both parallel to the baseline. The data that is outside the control limits are examined to be out of control. Thus, the main priority is to reduce the false alarm based on several historical consecutive data samples to establish an accepted or mean value as the baseline. The upper limit control (UCL) and lower limit control (LCL) are,
UCL = Mean + 3 x standard deviations (5) LCL = Mean − 3 x standard deviations (6) whereby, mean = mean of the historical data.
The association between the parameters (input) and COP (output) variables is often implemented in data analysis and methodological research [14]. Pearson’s, Spearman’s and Kendall’s correlation coefficients are the most commonly referred to measure of monotone association, for example, the normally and non-normally distributed data. Correlation is a bivariate analysis that measures the strength of association between two variables and the direction of the relationship. In terms of the strength of the relationship, the value of the correlation coefficient varies between +1 and -1. A value of ±1 indicates a perfect degree of association between the two variables. Pearson’s correlation coefficient is chosen because it has significant advantages for continuous data, which does not have obvious outliers [15].
Pearson’s correlation coefficient is given by,
(7)
METHODOLOGY
The following sections explain how to perform data pre-processing and methodology utilised for the current project.
Machine Learning Modeling of ANN
ANN is built with a requirement to follow the correct method of machine learning. The prerequisite for machine learning is data pre-processing, develop and validate the model [16].
Data Pre-processing
The most crucial part of data mining is data pre- processing. The real operation data gathered for the electrical chiller system are from Gas District Cooling in Presint 1, Putrajaya. Then, the Tukey method is applied to analyse the upper limits and lower limits in the gathering data for every parameter, as shown in Table 1. For the data cleaning, the outliers are removed and erase the constant value of the variables as the value is the same, which might affect the modelling later [13].
After data cleaning finished, select one out of four ECC’s in the district cooling plant that has the largest number of data available. Next, establish the weightage of the interaction between inputs and outputs and perform the feature selection. All features are being chosen for the first loop and classify data into training data and testing data using the 80/20 rule.
Progressing of Machine Learning Model
The Supervised learning algorithm is selected for the outputs data given because it is simple and more accurate than the unsupervised learning algorithms.
A guideline model of electrical chiller is constructed to illustrate the normal operation of the machine. The steps of the development of ANN begin with training and testing data are imported into MATLAB workspace.
The data must be standardised to differentiate bias and normal data. After that, establish the training input, which is the parameter of the machine and training output which is the COP. The ANN model is practised to train the data.
Table 1 List of available input variables
NO. VARIABLES UNITS
1 Chiller Water Suction Pressure kPa 2 Chiller Water Discharge Pressure kPa
3 Chiller Return Temperature ºC
4 Chiller Supply Temperature ºC
5 Chiller Water Flow m3/h
6 Heat Summation RT
7 Heat Totaliser RTh
8 Real Power Demand kW
9 Max Real Power Demand kW
10 Total Power Factor -
11 Frequency Hz
12 Total Real Power kW
13 Total Apparent Power kVa
14 Total Reactive Power kVar
15 Operation Time hour, h
Table 2 ANN configuration
Input mode Output mode Prediction structure
Chiller Return Temperature (oC) Coefficient of Performance
(COP) 3 hidden layer, 5 nodes, 10 nodes
and 15 nodes
Chiller Supply Temperature (oC) 4 hidden layer, 5 nodes, 10 nodes and 15 nodes
Chiller Water Flow (m3/hr) 5 hidden layer, 5 nodes, 10 nodes
and 15 nodes Real Power Demand (kW)
Model Validation
Validation and optimisation were investigated under the general chiller operation condition based on the actual results to predict the performance of the electric chiller model. The model was created and compared to obtain the best model for this research.
Based on the operation cleaned data, establish
the testing input and testing output, as shown in Table 1. Next, implement the testing input on the trained network to acquire the predicted output. The predicted output is used to find RMSE later. The total error (sum of error squares) for each value is the sum of the squares of the differences between the actual and the ANN predicted (output) data. This error was used as a measurement for the selection of the
optimal ANN architecture. RMSE is analysed between models with different hidden layers and neurons to choose the best model, which has the lowest RMSE as the calibration model.
Examine the Equivalence between Inputs and Output Variables
There are various parameters for electric chiller, including the operation time, which also needs to be observed. Therefore, all the input variables should be evaluated to ensure the correlation between output variables [14]. This is an essential and crucial step for engineers to supervise machine performance as well as to achieve the project’s objective. Firstly, Mean Square Error (MSE) is obtained from the ANN interface based on the previously selected model. This MSE illustrates the model performance and correlates with the actual output that will provide as the baseline reference. Fifteen sets of input data set have been established, and each data set has one input variable set to the mean value to display as a constant value in the data set while the rest remain as its previous condition. This data set is to train all models to acquire the MSE for every 15 models and analyse them with the baseline reference. The model is improving as expected if it is lower than the reference value. If the model has a higher value than the reference value, it presents the model has less accuracy and shows that the variable has a high impact in demonstrating the output that can affect the performance of the machine.
Lastly, to gather four models with the highest MSE values to illustrate that four input variables which are most highly corresponding to the output variables.
Development of Data-driven Predictive Model of Electric Chiller
From the previous method, the baseline reference model is generated. COP can be predicted from the ANN, and the control limits are applied to build the root cause analysis model. Basic ANN architecture was created through the process of finding the fault detection model. From the predicted COP, the Shewhart Chart Method applies in producing the limits as well as for each critical input variables for the construction of limits. After the limits are employed, the model can be used to detect the anomalies, and potential risk is alarmed in every 5 consecutive anomalies. Retest the model with the new testing data, which has a normal and abnormal condition and obtain the result to display the sensitivity of the model towards the failure condition.
RESULTS AND DISCUSSIONS
The following sections demonstrate the results obtained, followed by a detailed discussion of the findings.
ANN Network Modelling
Table 3 shows that all the models have a different value of the lowest RMSE when changed the hidden layers and neurons. The higher RMSE value will affect the model accuracy. Therefore, the model with the lowest RMSE, 0.0055 was chosen as a machine learning model of the electric chiller with 5 layers that Table 3 Comparison RMSE value between models
Neurons Layers
3 4 5
5 0.0125 0.0204 0.0055
10 0.0089 0.0127 0.0185
15 0.0093 0.0170 0.0252
have 4 hidden layers and 1 output layer, as shown in Figure 5. This model also has the least computational time to help increase efficiency and simplicity. From Figure 6, the regression fit of validation for the model achieved almost 100% because it has high prediction accuracy towards the outputs. Therefore, it is proven that the model is the most favourable.
Examine the Equivalence Between Inputs and Output Variables
As shown in Figure 7, The MSE value of the baseline reference is 21405 based on the performance of the model to the actual output from the ANN interface without changing the input parameter to constant.
Figure 5 Network architecture of the selected model
Figure 6 Regression fit of the model
It is compared with MSE values to obtain the critical parameter that affects the performance of COP.
When the parameters change to constant, the model accuracy increases and decrease, respectively. As a result, six parameters have been found increasing in MSE, which are,
1. Chiller Water Suction Pressure 2. Chiller Return Temperature 3. Chiller Supply Temperature 4. Chiller Water Flow
5. Real Power Demand 6. Total Real Power
Since the data value of real power demand and total real power is almost the same, these parameters are considered as one critical parameter. Therefore, five critical parameters affected the performance of ECC and formed new modelling.
Performance of ANN on Prediction Output
From Figure 9, the model constructed of ANN model illustrates that it is possible to predict the COP of the electric chiller by using the chiller water suction pressure, chiller return temperature, chiller supply temperature, chiller water flow and real power demand. These critical parameters are used to calculate the COP except the chiller water suction pressure. Therefore, ANN is capable of predicting the performance of ECC.
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Figure 7 Graph of comparison for each variable
Figure 8 Network architecture of the simplified model
Figure 9 Performance of ANN on prediction output
Root Cause Analysis Model
Shewhart control chart method is one of the statistical processes controls chosen to perform root cause analysis model based on UCL and LCL of each critical input parameter. The control limits help to find the anomaly that determines the root cause, as shown in Table 4.
Table 4 Control limits of input and output variables
Variables UCL LCL
Chiller Water Suction Pressure (kPa) 2.20 1.62 Chiller Return Temperature (oC) 13.29 9.39 Chiller Supply Temperature (oC) 6.14 4.05 Chiller Water Flow (m3/hr) 1203.47 975.32
Real Demand Power (kW) 2086.53 922.38
Coefficient of Performance (COP) 6.20 4.43
The new data is set to analyse the sensitivity of the selected network towards the anomalies. The data consists of normal and abnormal operating data for each critical parameter. Then, the models are tested to produce the alarm the ‘potential risk’ in every five consecutive anomalies.
Table 5 Model sensitivity towards data condition
Range of Data Data Condition Accuracy (%)
1-20 Bad Chiller Water Suction Pressure 100
21-40 Bad Chiller Return Temperature 95
41-60 Bad Chiller Supply Temperature 100
61-80 Bad Chiller Water Flow 100
81-100 Bad Real Demand Power 60
101-200 Normal 88
The model accuracy based on the calculation of the value outside the control limits is calculated in Table 5. The RCA model has high accuracy in detecting the anomalies by using Shewhart Control Chart.
The detection accuracy is around 95-100% when the abnormal data operation in range 1-80 tested.
However, the accuracy of detection slightly decreased to 60% when the data condition is bad real demand power due to unscheduled time operation when the operator turns on or off the power, and there is the warm-up value of power after turning on the power. For the case of normal data operation, a false alarm rate also implemented to identify how many alarms are being triggered in the data range 101- 200. It is only 88% accuracy, which means 12 out of 100 normal data are being declared as bad data and are not shown consecutively. Therefore, no potential alarm is trigger during the operation, and the model is safe to be applied.
CONCLUSIONS
Most of the past research chose certain parameters to observe the electrical chiller system performance analysis but did not determine the overall performance.
However, the same research on steam absorption chiller using machine learning as a reference. During data pre-processing, the outliers manage to remove for each 15 input variables and 1 output variable, which are divided between training and testing the
model of ANN. A model with 5 layers (4 hidden layers and 1 output layer) and 5 neurons are chosen due to the lowest root mean square error as compared to other models. Pearson R Correlation is carried out to measure the degree of relationship between input and output variables and found 5 critical parameters.
After that, the neural network model has predicted the output with a trivial mean absolute error of 0.0041 on testing, which is improved from 0.0055. In addition, the Shewhart chart has been implemented to control the limits of input and output variables when finding the RCA based on the statistical process control. The validation result of the predictive analytic model is comparable to another research work elsewhere, which used machine learning to predict the chiller plant model by using different data. Moreover, the present work obtained the predicted data for each parameter using actual data. Therefore, it can be concluded that the prediction from actual data is not enough to evaluate the performance of the electric chiller plants. Hence, it is clearly shown the anomaly can be detected from the data-driven data using the RCA model. As a recommendation, future works for improvement should focus on furthering the research to time series prediction model since there are many conditions to operate the machine using similar machine learning. Other than that, future research should also use the schedule data as the data-driven to reduce the outliers that can degrade the predictive models. Finally, use more data point to obtain a better trend for the results.
ACKNOWLEDGEMENT
The authors of this research would like to acknowledge the involved company from the Gas District Cooling (GDC) Plant 1 at Presint 1 in Putrajaya for providing the operation data.
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