Applying Data Mining Technology in Diagnosis for the Operation of Air conditioning System

This paper develops a diagnostic method for chiller plant operating anomalies. Use data mining techniques to clean up the abnormal data and clustering algorithm to cluster information of the experimental data. This study uses Kmeans, PAM and Hierarchical methods for clustering data and evaluated the clustering result by Dunn index. The Apriori algorithm is used to establish operating rules to find out the abnormal operation behaviors. This research collected a year running data of a study case for validation of the diagnostic method. The study case is a LEEDNC Gold certified R&D headquarters office building, which consists of 5 underground floor and 17 floors above the ground. In order to dehumidify and remove the cooling load of fresh air in the MAU, the office building uses 3.3°C chilled water provided by ice thermal energy storage system. The additional sensible heat in the building is removed by the fan coils, which are served by 8°C chilled water. The diagnosis results show that the study case had some control problems occurring in the chilled water pump, brine pump and cooling tower. This study proves that the data mining method applied in diagnosis for the operation of chiller plant can discover abnormal running conditions efficiently. INTRODUCTION Research Background and Motives The global economy took off since the 20th century, the nonrenewable energy has been consumed rapidly, and two oil crises shocked the world in the 1970s, various countries began to focus on the energy subject. Besides the technical development of renewable energy, the efficient use of energy is guaranteed to reduce current energy consumption. General commercial buildings are provided with a central monitoring system to record the air-conditioning equipment operating condition, the mass and long-term data record is free of professional control, that is a big waste. The primary cause for the energy waste of air-conditioning systems is that the air-conditioning run at an inappropriate operating point for long, poor maintenance and inappropriate equipment control degrade the performance, which are difficult to be detected directly, forming the meaningless waste of energy. Therefore, the purpose of this study is to develop a data diagnosis mechanism for air-conditioning systems to help the owners and maintenance personnel find out and eliminate the errors difficult to be detected directly, so that the air-conditioning systems can return to normal. Literature Review The "diagnostic application of data mining" and "system fault diagnosis theory" will be used as literature review for this study. (1)Review of diagnostic application of data mining: Cheng Fan et al. 2015 proposed a diagnostic framework for building operating data, and discussed the probability of using data mining and Knowledge Discovery. The results showed that the month, weekdays and hour were preferred for airconditioning service time clustering, and the association rule could be used in Knowledge Discovery effectively and could discover problems. Fu Xiao et al. 2014 proposed using data mining technology to improve the building operation benefit. A skyscraper in Hong Kong was taken as an example, the abnormal operation could be discovered effectively by using association rule, and the operating conditions when the anomalies occurred could be analyzed according to the problem data by using decision tree, contributing to solving problems. Ali Rahmani et al. 2014 proposed using graphicalization to study sequence data diagnosis discrete value. The shortest route was calculated by using the specified data route and minimum spanning tree, and the overlong route was removed to judge the discrete value, the line segments with too many action changes were discovered from the sequence time data effectively, so as to further discuss data problems. (2)Review of system fault diagnosis theory: House et al. 2001 proposed the Air-Handling Unit Performance Assessment Rules (APAR) for AHU. The diagnotor extracted the temperatures at all points of AHU, the damper opening signal and relative humidity of return air as the base of diagnosis, compared with the measured information. The experimental results proved that it could detect the AHU fault condition successfully and assist the maintenance personnel in troubleshooting. Kaldoft et al. 2002 developed a Building Energy Management System. The BEMS is a new type of building energy management system developed by combining building monitoring system with expert based diagnostics systems. It extracts various relevant parameters for BEMS in the monitoring system automatically, detects whether the equipment is abnormal or not automatically, diagnoses whether the equipment is faulted or not and gives a warning in time. It provides any intrasystem conditions, including whether the indoor temperature setting is too high or too low, and whether the supply air is normal or not. These messages provide a reference frame for equipment maintenance personnel to correct any anomalies of system instantly, so as to increase the equipment operating efficiency. Research Purposes Take a general view of the aforesaid references, there are many studies about the air-conditioning system fault diagnosis technique, multiple faults are diagnosed simultaneously, such as water shortage, excess water and abnormal power consumption, so as to stabilize system operation. The equipment faults or operation decision errors will influence the ISBN: 978-0-646-98213-7 COBEE2018-Paper201 page 579 4th International Conference On Building Energy, Environment overall system power consumption, so the fault diagnosis ability is necessary for the system. This study refers to the data mining for air conditioning diagnosis architecture proposed by Fu Xiao and Cheng Fan, with the air conditioning operating data of a commercial building in 2013, to discuss the probability of data mining application and abnormal operation mining. This study uses K-means, PAM and Hierarchical clustering algorithms and Dunn index to evaluate the optimum clustering algorithm as the clustering algorithm, so as to enhance the data similarity of experimental data. To match Apriori algorithm, the experimental data must be converted first, the numerical data are converted into class data, such as Low, Mid and High. RESEARCH METHOD AND PROCESS Research Process According to the collected data, the operating seasons are partitioned by clustering algorithm and the equipment operation association rule is established by association rule algorithm, and the time of abnormal operation is searched for backward by using association rule, so as to further analyze errors. The research structure process is shown in Figure 1: Figure 1 Research structure Introduction to case site The experimental case of this study is located in New Taipei City, Taiwan, it is a business office building, provided with an intelligent building monitoring system to measure and record the distributor power consumption, air-conditioning equipment power consumption and outside air temperature and humidity. The data recoded every 5 minutes. In this building, Monday to Friday are normal workdays, there is a little overtime and basic equipment power consumption on Saturday and Sunday. Air-conditioning system The case study of building air-conditioning system is variable primary flow chilled water system with the ice thermal storage system as shown in Figure 2 to Figure 4. The chilled water system supplies 8°C chilled water, the ice thermal storage system supplies 6°C chilled water, the night full ice storage is adopted and the operation is scheduled as 8 p.m. to 8 a.m. on the following day. Figure 2 System flowchart on chilled water side Figure 3 System flowchart on glycol side Figure 4 System flowchart on heat exchanger side Experimental Instruments The experimental data are derived from the intelligent building monitoring system record. The research facilities are shown in Table 1, including 2 chillers, 2 brine makers, 4 cooling tower and 17 water pumps. As the cooling tower water pump and charging pump are free of electric meter record and there is no operation record of spare pumps, they are excluded from the research facilities. There are 14 data obtained. Table 1 Equipment list Item qty. Symbol Number Chiller (Water) 2 CHU 01, 02 Chiller (Glycol) 2 CHU 03, 04 Cooling Tower Fan 4 CT 01~04 Primary Chilled Water Pump 2 CHP 01, 02 Glycol Pump for Heat Exchanger 2 BHP 03, 04 Chilled Pump for Heat Exchanger 2 CHP 03, 04 RESEARCH THEORY Data purging General description of data analysis. The raw data file lacks data declaration before data mining, including the data type of ISBN: 978-0-646-98213-7 COBEE2018-Paper201 page 580 4th International Conference On Building Energy, Environment error (e.g. numbers stored in character string), classification tag of error, unknown or unexpected character code. Therefore, the original data preprocessing or data purging is required before data mining. In the following will described the data loss, specific data, outliers, and data inconsistency, respectively. Data loss The experimental data are extracted from the data record database of intelligent building monitoring system, the data are collected by multiple collectors and fed back to the terminal server for record. There will be data loss resulted from a part of faulted collectors during data transmission. When one or multiple values are lost, the statistical analysis cannot be implemented, the missing value can be neglected or the data can be interpolated, and this study neglects the missing value. Specific data In actual state, the statistical table shall be free of particular value, the ideal state is to process particular value before analysis. As the data in this study are record data, free of numerical computation, there is no such a situation. Outliers The outlier is a particular value detected, not required to be removed, but a basis of judgment and data distr