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This paper depicted the novel data mining based methods that consist of six models for predicting accurate future heating and cooling load demand of water source heat pump, with the objective of enhancing the prediction accuracy and the management of future load. The proposed model was developed to ease generalization to other buildings, by making use of readily available measurements of a comparatively small number of variables related to water source heat pump operation in the building environment. The six models are - tree bagger, Gaussian process regression, multiple linear regression, bagged tree, boosted tree and neural network. The input parameter comprised the prescribed period, external climate data and the diverse load conditions of water source heat pump. The output was electrical power consumption of water source heat pump. In this study, simulations were conducted in three sessions - 7-day, 14-day and 1-month from 8th July to 7th August 2016. The forecast precisions of data mining models were measured by diverse indices. The performance indices which were used in assessing the prediction performance were - mean absolute error, coefficient of correlation, coefficient of variation, root mean square error, mean square error and mean absolute percentage error. The mean absolute percentage error results for 7-day future energy demand forecasting from tree bagger, Gaussian process regression, bagged tree, boosted tree, neural network and multiple linear regression were 3.544%, 0.405%, 1.703%, 1.928%, 2.592% and 13.053%, respectively. Moreover, when the proposed data mining model performance was compared with the existing studies, the mean absolute percentage error of 2.515% was found out for the first session, 7-day. The results also showed that the six models were efficient in foreseeing the abnormal behavior and future cooling and heating load demand in the building environment. [ABSTRACT FROM AUTHOR]

With the development in information technologies, today's building energy consumption can be well monitored by the building energy management systems. However, in most real applications there is no clear definition of abnormal building energy consumption. To overcome this limitation, this work proposes a novel deep learning based unsupervised anomaly detection framework that includes recurrent neural networks and quantile regression. Moreover, this framework is able to produce a prediction interval to detect and evaluate abnormal building energy consumption. The framework has been applied to analyze the energy data collected from three different residential houses, and anomaly detection results are evaluated by the quantile regression range. The research results can provide promising solutions for building managers to detect abnormal energy performance, and is also valuable to assess the level of anomalies and spot opportunities in energy conservation. [ABSTRACT FROM AUTHOR]

The variable refrigerant flow (VRF) system has changeable energy performance due to shifty meteorological conditions and unstable internal factors (e.g. complex control strategies, various part load ratios). It is difficult to determine the causes of drastic energy consumption variations are normal factors or faults. This study therefore proposed an energy diagnosis method for VRF systems based on data mining techniques and statistical quality control approaches. The correlation analysis is employed to select key factors and the DBSCAN method is used to remove the transient data and outliers. Besides, the system power consumption is predicted using the SVR algorithm. The EWMA control chart is used to diagnosis the system energy performance and it is comparatively analyzed with the x ¯ -R and CUSUM control charts. The reliability of the proposed method is verified by diagnosing the system energy in refrigerant undercharge and overcharge conditions, respectively. Results show that the SVR method is reliable to predict the system energy consumption with a R 2 value of 0.9974. In addition, the EWMA control chart can significantly improve the VRF energy diagnosis performance compared to the x ¯ -R and CUSUM control charts. It achieves high correctly diagnosis ratios at both refrigerant undercharge and overcharge conditions. [ABSTRACT FROM AUTHOR]

The outdoor air-side fouling fault is almost inevitable for the practical variable refrigerant flow system. Fouling fault grows gradually and naturally causing increasingly energy penalty and performance degradation to the system. Implementing a proper and reliable fault detection and diagnosis strategy is crucial for practical systems keeping away from fouling fault and maintaining optimal operations. However, traditional model-based and data-driven methods cannot work in practical systems due to the lack of critical sensors and interpretation for model reliability, respectively. Therefore, this study proposes a data mining approach to identify and isolate fouling faults using only built-in sensors. Density-based spatial clustering of applications with noise(DBSCAN) is used for data pre-processing. The classification and regression tree(CART)-based classifier is employed for fault detection. Based on Pearson’s correlation analysis, a multiple linear regression(MLR)-based fault indicator is developed for fault isolation. A case study on a 29.8 kW cooling capacity R410A variable refrigerant flow system is used to validate the proposed strategy. Four levels of foulings are experimentally investigated under three cooling conditions. Results reveal that the proposed strategy correctly identifies 98% of fouling data using only three built-in sensor measurements. It also isolate fouling data from both normal and refrigerant charge fault data. [ABSTRACT FROM AUTHOR]

• The proposed strategy aims at system- and component-level fault types of VRF systems. • An infrequent fault type of VRF systems is studied. • The FDD strategy is based on hybrid data mining methods. • The results are explained and supported by thermodynamics theory. • The established models can accurately detect and diagnose faults of VRF systems. VRF systems are always vulnerable to kinds of faults. Fault detection and diagnosis research should not only accurately identify these faults, but also be capable of obtaining explanations and support in thermodynamic theory. In this study, a strategy is proposed for four types of VRF system faults, including system-level and component-level. The strategy is based on hybrid data mining methods and analyzes the thermodynamic interpretation of the results at the same time. The first preprocessing step eliminates the effect of noise caused by defrosting action in heating mode. We apply unsupervised principal component analysis for feature extraction to reduce the dimensions of data sets. The correlation between principal components and features are investigated. Supervised Gauss naïve Bayes is used to establish the fault detection model with an accuracy of 98.6%. Besides, infrequent fault type is often difficult to be studied because of lacking sufficient data. Therefore, RUSBoost algorithm is used to solve the unbalanced set problem, and the results show enough competitiveness in the comparison of similar algorithms and online testing. Conclusive remarks confirm the truth that the proposed strategy enjoys high versatility, accuracy, and robustness. [ABSTRACT FROM AUTHOR]