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Accurate typhoon intensity estimation is vital for timely risk warning and hazard assessment. The task is challenging since the subcategories of the typhoon have visually similar appearances. The local context extracted from the typhoon center region and the global context of cloud pattern is the most discriminative features for intensity estimation. However, the existing method does not utilize local and global contexts together. Furthermore, the interactions between local and global contexts remain unexplored. To make a better prediction, this paper proposes a local and global context attention mechanism-based fusion network that consists of a spatial attention fusion block and a channel attention fusion block. The channel attention fusion block infers an optimal channel fusion weight by exploiting the inter-channel relationship. The spatial fusion block learns the best inter-spatial relationship of features. Extensive experiments on DeepTi and TCIR benchmarks demonstrate the effectiveness of our method, and the proposed network achieves new state-of-the-art. The code is at https://github.com/chen-huanxin/RBCFN. • Local and global information fusion improve estimation accuracy. • A multi-level framework aggregates information from multiple areas. • Spatial and channel attention fusion improve feature representation ability. • Attention mechanism effectively fuses multi-modal information. [ABSTRACT FROM AUTHOR]

Separation of acetylene (C2H2) and carbon dioxide (CO2) is of prime importance in the petrochemical industry, but their similarities in molecular size and volatility make it particularly difficult. A desired adsorbent for highly efficient C2H2/CO2 separation should have high selectivity and C2H2 uptake simultaneously, and the introduction of functional groups into MOFs is an effective strategy to achieve this purpose. Herein, we report a titanium‐based MOF material (MIL‐125‐NH2), featuring the suitable pore sizes and amino‐functionalized pore surfaces to show the preferential adsorption of C2H2 over CO2 with both high C2H2 uptake and C2H2/CO2 selectivity. Theoretical simulation results indicate that the preferential C2H2 adsorption is mainly attributed to multiple hydrogen bonding interactions between C2H2 molecule and amino functional groups. [ABSTRACT FROM AUTHOR]

Data-driven approach has emerged as a popular research method for fault detection and diagnosis (FDD) of variable refrigerant flow system. However, one serious issue has been raised in recent studies, which shows that training classifiers with datasets which suffer of imbalanced class distributions can significantly influence the final classification accuracy. So, aiming at the imbalanced dataset environment problem of variable refrigerant flow (VRF) system based on data-driven fault detect and diagnosis, we conducted a study. First of all, through comparative research, it is concluded that the geometric mean accuracy of the fault detection in the VRF system has reduced an average of 42.49% due to the imbalanced data environment. Then we proposed a Principal Component Analysis-Synthetic Minority Oversampling Technique (PCA-SMOTE) method to save this problem, and verified the reliability and versatility of the proposed method with three types of modeling algorithms commonly used in VRF systems. The result shows that this method not only has obvious improvement effect, but also has strong versatility. In the end, we gradually reduce the number of minority from 50 to 10 to explore minimum fault data requirements of this method. The final result shows that this method can still ensure that common machine learning algorithms can effectively detect and diagnosis the VRF system fault when only 10 fault data are included. [ABSTRACT FROM AUTHOR]

• An improved deep learning model based on tree structure is proposed in VRF system. • Pearson's correlation coefficient combines with isolation forest for data preprocessing • The physical significance of outliers in VRF system is explained. • The IF-CF model does not require complex hyper-parameter optimization strategy. This paper presents a hybrid deep forest approach for outlier detection and fault diagnosis. Isolation forest algorithm is combined with Pearson's correlation coefficient for outlier detection. The physical significance of outliers detected by the proposed algorithm is explained by origin analysis, which is rarely mentioned in existing studies. In addition, a novel non-neural network deep learning model-cascade forest model is proposed to fault diagnosis of HVAC system for the first time to achieve high precision accuracy in low-dimensional features. The proposed approach is validated with the refrigerant charge fault of VRF system. The results show that the isolation forest algorithm can improve the performance of fault diagnosis model and the mainly outliers of VRF system are defrosting data. The IF-CF model has short operation time, and high accuracy in low-dimensional features. When the dimension drops to 6, the accuracy of the IF-CF model is 94.16%, which is 5.26%, 10.02%, 5.87% and 3.34% higher than the IF-MLP, IF-BPNN, IF-SVM and IF-LSTM models, respectively. Moreover, IF-CF model does not require complex hyper-parameter optimization strategy because its maximum accuracy difference in different hyper-parameters is 2.04%. This study is enlightening which may inspire the potential of outlier detection technology and deep learning in HVAC field. [ABSTRACT FROM AUTHOR]

• A novel fault diagnosis strategy is proposed using the GMM method. • The principal component analysis is selected for data dimensionality reduction. • Evaluations are made using four faults data of the VRF system experiments. • The established PCA-GMM has better fault diagnosis performance and reduced runtime. The timely fault diagnosis of HVAC systems is important for building energy saving, equipment maintenance and indoor comfort. The Gaussian mixture model method has rarely been studied in the fault diagnosis application of HVAC systems. Therefore, a novel fault diagnosis strategy is proposed based on the Gaussian mixture model (GMM) method for the variable refrigerant flow air-conditioning system. To reduce excessive input variables resulting in large model complexity and long running time, the principal component analysis approach (PCA) is used to perform data dimensionality reduction. Therefore, the fault diagnosis model combining the Gaussian mixture model and principal component analysis is established, which is evaluated using the four types of faults of the variable refrigerant flow system. These faults include refrigerant undercharge, refrigerant overcharge, outdoor unit fouling and four-way reversing valve faults. Experiments are carried out under three heating conditions. Results show that the PCA-GMM approach can effectively reduce the running time. Especially for the VVV type model, the running time is reduced from 176.78 s to 15.18 s. Meanwhile, established PCA-GMMs still have good fault diagnosis correct rates when the input data dimension is reduced. Especially, some PCA-GMMs have fault diagnosis correct rates of over 99% when the number of principal components exceeds 7. [ABSTRACT FROM AUTHOR]

Energy consumption of heat pump system accounts for a large part of the total building energy consumption, and the energy-saving operation of heat pump system has always been the focus of researchers. A promising solution to tackling energy wastes during system operations is anomaly detection. In this study, we propose an anomaly detection method for GSHP system in a public building based on mode decomposition based LSTM and statistical modeling method Grubbs' test. The system energy consumption is predicted using mode decomposition based LSTM algorithm, and the difference between predicted value and actual value is used to detect the abnormal system energy consumption by Grubbs' test. Results show that detected anomalies can be summarily divided into three categories (parabola anomaly, abrupt anomaly and time related anomaly) depending on their characteristics, and the rationality of detected anomalies are evaluated through field investigation and expert knowledge. This work is enlightening and indicates that the proposed method would efficiently detect the abnormal performance of GSHP system, and find out unreasonable operating patterns. [ABSTRACT FROM AUTHOR]

• We proposed a novel modal decomposition based ensemble learning method for GSHP energy consumption prediction. • Variational mode decomposition and empirical mode decomposition were used to decompose the energy consumption data. • The method was compared with two common used machine learning models with optimization process. • The method showed outstanding effect on improving the performance of GSHP energy consumption prediction. This study presents a case study of office buildings using modal decomposition based ensemble learning method to forecast energy consumption of ground source heat pump systems (GSHP). Conventional machine learning methods have uncertainty in practical application as there are lots of stochastic terms in the structure of the algorithm. Therefore, ensemble learning models are proposed to ameliorate the problem. In this paper, the prediction potential of modal decomposition based ensemble learning models are investigated while providing a comprehensive comparison on different single models and ensemble learning models in the building field. Results show that the proposed VMD based ensemble learning models have remarkable advantages in GSHP energy consumption prediction comparing with other machine learning methods, and the prediction performances of VMD based ensemble learning models measured by RMSE and MAE are 30–50% better than common machine learning methods. This work is enlightening and indicates that VMD based ensemble learning models fit well with energy consumption prediction, which could bring more efficient and concise solutions for GSHP energy consumption predictions. [ABSTRACT FROM AUTHOR]

Highlights • AI-based models are proposed for utility district-level load management. • Two different climate zones actual energy consumption and climate data is used. • The potentials of PRGBNNs & GDALBNNs models is investigated at five different PS states. • AI-based models show several benefits in forecasting accuracy and model development. • AI-based models compared with three existing model & show accurate and better performance. Abstract Increasing sustainability demands initiate estimating various design and control opportunities for classifying energy-efficient plan ever more significant. These conditions demand simulation algorithms which are not only fast, but also accurate. Artificial intelligence (AI) enables efficient mimicry of bulk energy consumption control while producing results much faster than data-mining and machine learning models. This study proposes two AI based approaches for utilities bulk energy consumption prediction, control and management. Two different zones actual environmental and energy consumption data are obtained for input feature selection and modeling analysis. Each zone is categorized into five features parameter selection (PS) states. Each PS state is further divided into four different hidden neurons (HD) and hidden layers of the model's network. The forecasting duration is based on 1-month and 1-year ahead intervals for medium-term (MT) and long-term (LT) respectively. Further the current proposed model's performance is compared with three existing models. One of the promising findings in this research is that substantial improvement in prediction accuracy applying features extracted by PS-3 and PS-5. Results show that AI models are powerful in solving complex and nonlinear patterns of raw data. This study renders optimal decisions can be projected while utilities energy supply strategy & control, capacity expansion, capital investment research market management, revenue analysis and future load requirement forecasting. [ABSTRACT FROM AUTHOR]

Abstract Short-term load prediction at the district-level is essential for feeders, substations, consumers and transformers starts from 1-h to one-week ahead. Though, the critical problem is in the design of complex load characteristics, switching operations and a large number of junctions at electrical distribution feeders. To overcome the design challenges, this research conducted an analysis of short-term energy requirement forecasting for district-level by applying into two deep learning models. Present, the booming increase of deep learning (DL) methods give them encouraging options to data-driven techniques. However, DL gives exceptional ability in handling the non-linear complex relations, model computation performance and complexity are of attention. Further, the two different climate zones data used for modeling analysis. The input feature parameter sets classified into two parts: i) features selection-I (FS-I); and FS-II. The FS-I comprises based on seven environmental parameters, including various limits of energy consumption data sets and the FS-II contains the different sixteen feature variables. The FS-II mean absolute error is <2.05% and <1.577% of OSSB-NN and BFGS-QNB respectively. The deep learning models achieved higher forecasting accuracy at the proposed structure of the model's network at different hidden neurons. Based on the current trend and the comparison results, a structure of district energy prediction and future research direction proposed for district-level design, planning, and services. Highlights • Two deep-learning models used to forecast future load demand of two climate zones. • Models will play essential role in the future district energy forecasting. • Higher accuracy obtained for short-term horizons leads to economic advantages. • The MAPE of BFGS-QNB forecasting model is <3.679%. • A framework is deep learning proposed for design, planning, and service. [ABSTRACT FROM AUTHOR]

Highlights • Three data-mining models are applied for cooling load demand forecasting. • Short and medium-term intervals are applied for future energy demand forecasting. • Environment variables effect on cooling load demand is also analyzed. • Model's performance and accuracy has been estimated at selection of different hidden neurons. • The best cooling load forecasting performance achieved at the short-term interval. Abstract In this study, data-mining techniques comprising three forecasting algorithms for accurate and precise cooling load requirement prediction in the building environment, with the primary aim and the objective of improving the load management are applied. Three state-of-the-art cooling load prediction algorithms are – multiple-linear regression (MLR) model, Gaussian process regression (GPR) model and Levenberg–Marquardt backpropagation neural network (LMB-NN) model. The Pearson correlation analysis is practiced calculating the correlation between actual cooling load demand and input feature variables of climate parameters. The impact of climate variability on the building load requirement is also analyzed. Forecasting intervals are divided into two basic parts: (i) 7-day ahead prediction; and (ii) 1-month ahead prediction. To assess the prediction performance, four performance evaluation indices are applied, which are: (i) coefficient of correlation (R); (ii) mean absolute error (MAE); (iii) mean absolute percentage error (MAPE); and (iv) coefficient of variation (CV). The model's performance is compared with the selection of different hidden neurons at different load conditions. The MAPE for 7-day ahead prediction interval by MLR, GPR and LMB-NN model is 13.053%, 0.405% and 2.592%, respectively. Furthermore, the data-mining algorithms are compared and validated with the previous study, and the MAPE of Bayesian regularization neural networks is calculated 2.515% for 7-day ahead prediction. It was witnessed that the algorithms could be applied to facilitate the building cooling load prediction, by applying a relatively limited number of parameters related to energy usage as well as environmental impact in the building environment. The forecasting results show that the three algorithms are effective in predicting the irregular behavior in the data as well as cooling load demand prediction. [ABSTRACT FROM AUTHOR]

Medium-term and long-term energy prediction is essential for the planning and operations of the smart grid eco-system. The prediction of next year and next month energy demand of grid station, independent power producers, commercial, domestic and industrial consumers are allowed administrators to optimize and plan their resources. To address the forecasting problems, the basic intention of this study is to propose an accurate and precise medium and long-term district level energy prediction models employing the machine learning based models which are: 1) artificial neural network with nonlinear autoregressive exogenous multivariable inputs model; 2) multivariate linear regression model; and 3) adaptive boosting model. Based on environmental and aggregated energy consumption data as the model's input and output, the load prediction interval is further classified into three main parts, 1-month ahead forecasting, seasonally ahead forecasting and 1-year ahead forecasting. Feature extraction, data transformation and outlier detection are performed through different data tests. The prediction results intimate that the intended models cannot only increase the forecasting accuracy contrasted with previous forecasting models but also produce adequate forecasting intervals in the smart grid environment. Additionally, these techniques describe an essential step-forward, consolidating the spatiotemporal use of energy inconstancies and variations of district level and strong forecasting capabilities of energy usage requirement in future perceptive. [ABSTRACT FROM AUTHOR]

The substantial measure of energy usage connected to the building atmosphere supports and sustains power usage modeling diligence. Amongst the numerous strategies to elaborate energy methods, supervised based machine learning approaches are immeasurable alternative to circumvent the inconvenience correlated to various engineering and data mining approaches when measured/observed data are accessible. This research depicts an analysis of electricity requirement forecasting by supervised based machine learning models with the limited data information. The power usage or energy consumption data is collected from power transmission and distribution networked organization independent system operator New England for one-year ahead energy forecasting. Moreover, energy consumption data is categorized into monthly, seasonally and yearly basis to foresee the performance for short-term, medium-term and long-term as well. Four-supervised based machine learning models employed for energy forecasting which are: i) Binary Decision Tree; ii) Compact Regression Gaussian Process; iii) Stepwise Gaussian Processes Regression; iv) Generalized Linear Regression Model. The input variables comprise the limited external environmental data, day-type/hour-type and the net energy consumption of various types of load. The output is the total energy demand of the building power usage. Modeling studies are escorted for expected energy demand in future perceptive based on Independent System Operator New England data. The performance evaluation indices applied in evaluating the model's forecasting error are coefficient of variation and mean absolute percentage error. In autumn season, the best MAPE and CV of the binary decision tree is 0.809% and 1.359% respectively for seasonal forecasting, and is 0.989% and 1.601% respectively for yearly forecasting. It is observed that the accuracy in forecasting is modest in the autumn season. In yearly prediction, the MAPE and CV of compact regression Gaussian process, stepwise Gaussian processes regression and generalized linear regression are 3.245% and 3.650%, 4.039% and 4.860%, 5.118% and 5.927% respectively. The machine learning model's performance compared and validated with the actual energy consumption, existing artificial neural network model and the mean absolute percentage error and coefficient of variation found 2.416% and 3.290% respectively for yearly prediction. It is depicted that including the utilization of limited energy usage and environmental data as one of the model's input variables, the electricity forecasting precision is more accurate, precise and can be improved. [ABSTRACT FROM AUTHOR]

This paper examines the data-mining and supervised based machine learning models for predicting 1-month ahead cooling load demand of an office building, including the primitive intention of enhancing the forecasting performance and the accuracy. The data-mining and supervised based machine learning models include; regression support vector machine, Gaussian process regression, scaled conjugate gradient, tree bagger, boosted tree, bagged tree, neural network, multiple linear regression and bayesian regularization. The external climate data, hours/day in a week, previous week load, previous day load and previous 24-h average load are applied as input parameters for these models. Whereas, the output of the models is the electrical power required for water source heat pump. A water source heat pump located in Beijing, China, is selected for examining 1-month ahead cooling load forecasting, i.e., from July 8 to August 7, 2016. In this paper, simulations are classified into three sessions: 7-days, 14-days and 1-month. The forecast performance is assessed by computing four performance indices such as mean square error, mean absolute error, root mean square error and mean absolute percentage error. The mean absolute percentage error for 7-days ahead cooling load prediction of the water source heat pump from data-mining based models, Gaussian process regression, tree bagger, boosted tree, bagged tree and multiple linear regression were 0.405%, 3.544%, 1.928%, 1.703% and 13.053% respectively. While, mean absolute percentage error of 7-days ahead forecasting in case of machine learning based models such as a regression support vector machine, Bayesian regularization, scaled conjugate gradient and neural network were 12.761%, 2.314%, 6.314%, 2.592% respectively. The percentage forecasting error index proved that the results of data-mining based models are more precise and similar to the existing machine learning models. The results also demonstrate that the better performance and efficiency in foreseeing the abnormal behaviour in forecasting and future cooling load demand in the building environment. [ABSTRACT FROM AUTHOR]

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]

Energy consumption models play an integral part in energy management and conservation, as it pertains to buildings. It can assist in evaluating building energy efficiency, in carrying out building commissioning, and in identifying and diagnosing building system faults. This review takes an in-depth look at energy-demand prediction models for buildings in that it delves into recent developments in building energy approaches used to predict energy usage. By enlisting current approaches to the modelling of buildings, methods for building energy simulations can be categorized into four level classes as follows: (i) data-driven approaches; (ii) physics-based approaches; (iii) large scale building energy forecasting approaches; and (iv) hybrid approaches. The focus of this review is to target the data-driven approach and large-scale building energy predicting-based approaches. Here the data driven approaches can be categorized by (1) artificial neural network based approaches; (2) clustering based approaches; (3) statistical and machine learning-based approaches; and (4) support vector machine based approaches. From there, the type of data-driven based approach is further grouped by (a) benchmarking models; (b) energy-mapping models; (c) energy forecasting models; and (d) energy profiling models. Large-scale building-energy prediction techniques is then categorized as follows: (1) white-box based approaches; (2) black-box based approaches, and (3) grey-box based approaches. The current study explores first-rate data-driven based approaches about building energy analysis for industrial, commercial, domestic, etc., within a rural and urban setting. This review paper is based on the necessity of identifying points of departure and research opportunities for urban and rural-level analyses of building level energy performance. A variety of issues are explored which include: energy performance metrics; end-use of different building types; multiple levels of granularity; and urban and rural scales. Each technique encompasses a variety of input information as well as varying calculations or simulation models along with furnishing contrasting outcomes that suggest a variety of usages. A thorough review of each technique is presented in this study. This review highlights strengths, shortcomings, and purpose of the methods of numerous data-mining based approaches. A comprehensive review of energy forecasting models that are specified in the literature part is also provided. [ABSTRACT FROM AUTHOR]

This review paper focuses on the various modeling practices for the identification and apprehension of non-technical losses. The modeling practices are extremely vital to develop, upsurge energy performance, examine and foresee the performance of power transmission & distribution of the electrical system. The data mining based models are innovative and have the subsistence to examine an enormous potential of energy consumption records and performing area profile for preparing housing zone directing the electricity effective living. In this concern, support vector machine model, which classifies illegal customers is a form of advanced mix evolutionary neural network model. Optimum-path forest clustering process is activated to recognize legitimate and irregular profiles of an industry as well as commercial customers to find out theft of electricity. Real time state estimation technique determines a state approximation method in the actual stage for every conversion (transformation) point. Aforementioned allows us to regulate the parts to the maximum extent of non-technical losses through the radial distribution method. The support vector machine with genetic algorithm advances a hybrid method for the non-technical loss investigation and provide an automated assistance to dominate the electricity theft. This model is simplified version of support vector machine. Decision tree and Bayesian regularization networks are appropriated to identify the several kinds of patterns of losses in the electrical system. These practices have been accompanied concerning testing and validation for power system losses in the experimental laboratory. It operates in an influence tool intended to expedite the investigators and scientists. It assists short of spending a massive amount of money, time and energy in experimental events. Prior fabrication modeling methods are remarkably significant in replication of diverse kinds of solar electrical systems. Accordingly, this study concentrates on the base of modeling methods not only saves time but, also preserves the monetary investment in the electrical system. The benefit and imminent opportunity of modeling practices are also conferred in the review paper. [ABSTRACT FROM AUTHOR]

An intelligent fault diagnosis network for variable refrigerant flow air conditioning system is proposed in this study. The network is developed under the foundation of bayesian belief network theory, which comprises two main elements: the structure and parameters. The structure obtained by machine learning and experts’ experiences illustrates the relationships among faults and physical variables from the qualitative prospective, and its parameters (including prior probability distribution and conditional distribution) describe the uncertainty between them quantitatively. Once the structure and parameters are determined, the posterior probability distribution which can be used to complete fault diagnosis and isolation will be calculated by some algorithms. In comparison with other fault diagnosis approaches, the proposed approach can make full use of performance information. Moreover, it is more reasonable and precise to express the relationship between faults and variables rather than Boolean variables. Evaluation was conducted on a variable refrigerant flow air conditioning system, which demonstrated that this strategy is effective and efficient. [ABSTRACT FROM AUTHOR]

A rational and reliable energy benchmark is useful for understanding and enhancing building performance while most buildings cannot provide sufficient information for a detailed energy assessment. This work presents a systematic methodology of developing dynamic energy benchmarks for individual office building with very limited information. Simultaneously, an energy consumption rating (ECR) system is established to provide vertical energy assessment for individual office building in a short time span, i.e. hourly. Based on the data produced by DOE prototype large office building model performed in the EnergyPlus environment, this study is conducted in three steps: (1) Step 1: Data preparation; (2) Step 2: Development of the dynamic energy benchmarks; and (3) Step 3: Evaluation of the dynamic energy benchmarks and ECR system. Based on the decision tree analysis, the system energy consumption is classified into eight patterns by few commonly accessible weather and time variables, i.e. outdoor dry-bulb temperature, relative humidity, day type and time type. Then, four energy benchmarks are developed according to four energy consumption patterns on weekdays. To verify the effectiveness of the proposed dynamic energy benchmarks, it is used to evaluate the building energy performance on September, October and November, respectively. Besides, comparative analysis is conducted between the energy baseline (i.e. the same benchmark is used for all energy consumption patterns) and proposed dynamic energy benchmarks. Accordingly, the hourly ECRs were calculated using energy baseline and proposed dynamic energy benchmarks, respectively. Results showed that the energy baseline can be improved by using the proposed dynamic energy benchmarks. And the proposed method is capable of evaluating the energy performance of information poor office buildings. [ABSTRACT FROM AUTHOR]

This paper presents a statistical training data cleaning strategy for PCA-based chiller sensor Fault Detection, Diagnosis and Data Reconstruction method. Finding and removing outliers from the original training data set, the training data quality can be improved by the presented data-cleaning strategy. This can enhance the efficiency of the fault detection and increase the accuracy of the data reconstruction. Outliers cannot be easily found in the original data set used for training the PCA model. These outliers would severely affect the projection directions of PCA's two orthogonal subspaces (PC subspace and Residual subspace). Therefore, the threshold of Q -statistic is changed by the unexpected projection subspaces so that the detection efficiency of the sensor fault is decreased. The Euclidean distance was employed as an index to detect outliers from the original training data. In order to achieve optimal training data for the sensor FDDR, the z -scores of each sample's Euclidean Distance were employed as the key to remove the outliers. A field measured data set of a screw water-cooled chiller was used to validate the presented strategy. Results demonstrate that the quality of the training data is optimized and sensor fault detection efficiency, as well as the reconstruction data accuracy, is improved when compared to the normal PCA method. [ABSTRACT FROM AUTHOR]

The practical operating conditions of thermoelectric products, such as the input power source and the thermal resistance of hot side heat exchanger, are different from the theoretical study. Thus the equations, which are used to estimate the practical maximum cooling performance just according to the datum in datasheet of commercial thermoelectric module (TEM), are given. The nested loop method is adopted to solve the numerical model. This study provides a method to choose a suitable TEM for thermoelectric product to meet the application requirement. It finds that the minimum cold side temperature increase and the voltage for achieving the minimum cold side temperature step decrease with the increase of thermal resistance of hot side heat exchanger, respectively. The maximum temperature difference increase and the voltage for achieving the maximum temperature difference step increase with the increase of thermal resistance of hot side heat exchanger, respectively. According to the dimension, three kinds of thermoelectric module, bulk TEM, miniature TEM and micro TEM, are studied. The novel scale effect are discovered by comparing these TEMs. It found that the step-change phenomenon become more and more obvious with the decrease of the dimension of thermoelectric module. The influence ratio of thermal resistance of hot side heat exchanger on the maximum cooling performance increases and the influence ratio of input power source decreases from macro to micro, respectively. It forecasts that there exists a critical value for the dimension of thermoelectric module, when the dimension of thermoelectric module is smaller than this critical value, the maximum voltage or current of thermoelectric module is constant and does not change with the thermal resistance of hot side heat exchanger. [ABSTRACT FROM AUTHOR]

Highlights: [•] The scaling effect and supercooling effect affect the performance of MTEM. [•] The voltage for achieving the maximum cooling capacity experiences step decrease. [•] A step-change voltage of MTEM improves the temperature precision of pulse laser. [•] We develop a curve fitting equation to provide more accurate temperature. [ABSTRACT FROM AUTHOR]

Highlights: [•] The more the porosity, the lower the pressure inside FADS. [•] The higher the porosity, the poorer the uniformity of air velocity distribution. [•] The effect of fiber diameter is similar with the effect of porosity. [•] Increasing air flow rate aggravates the nonuniformity of air velocity distribution. [•] Results would help guide the optimum design of FADS in penetration mode. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a self adaptive chiller sensor fault detection strategy based on Principal Component Analysis (PCA) method, namely a self-Adaptive Principal Component Analysis (APCA) method. The original data set used to train the PCA model usually contains some error samples, whose useless residual subspace information make the threshold of Q-statistic higher than the expected threshold. This leads to a low sensitivity and low fault detection efficiency at low sensor fault levels. APCA is developed to automatically remove error samples in the original data set in order to improve fault detection efficiency especially for temperature sensor faults with absolute magnitude less than 1°C. The self adaptive process of APCA has been presented and been compared with the Normal Principal Component Analysis (NPCA) method. The APCA strategy is validated by the operational data of a screw chiller system in a real electric factory. The results show that the APCA method can significantly enhance the fault detection efficiency, and the symmetry for positive and negative fault levels with same absolute magnitude becomes better than NPCA method. [Copyright &y& Elsevier]

Abstract: The microscopic heat transfer between solid and solid at cryogenic temperatures exists in many application fields. This paper employed the Laser Photothermal Method (LPM) which is a transient and non-contact method to measure the Thermal Contact Resistance (TCR) between solid and solid in the temperature range of 70–290K and the pressure range of 0.2–0.7MPa. This paper analyzed the effects of the temperature and the contact pressure on the TCR at interfaces. The relationship between the TCR and the temperature at certain contact pressure was established, and the explanation about this phenomenon was given. Following, the TCR of SS 304–AlN, SS 304–Cu and SS 304–SS 304 were compared at different temperatures and contact pressures. [Copyright &y& Elsevier]

Abstract: No existing diffuser models may be used to effectively describe the fabir air dispersion system (FADS), a new ventilation terminal made of ploymer. A porous media model was proposed to describe FADS in present work. And the corresonpongding mathematical model was developed for the airflow through FADS. The characteristics of airflow through FADS was predicted using the commercial software FLUENT with the standard k − ɛ turbulent model. The simulation results were well matched with the corresponding experimental value suggesting that it is feasible to describe FADS using the porous media model. Results showed that the air in the central zone flows forward with a decreasing velocity along the flow direction, and the air in the porous fibre eventually creeps out through its tiny-size holes at very low speed in the direction perpendicular to the porous fibre. The airflow close to the FADS is evenly distributed along the flow direction with a very low velocity. It is also concluded that the internal total pressure and dynamic pressure decrease slightly, while the internal static pressure increases slightly with a decaying increment along the flow direction of the FADS. [ABSTRACT FROM AUTHOR]

• 1-D CNN is an innovative approach utilized for fault diagnosis in VRF system. • 1-D CNN models achieves good accuracy and is capable of fault diagnosis. • Enhanced the method to an ensemble model by using parallel 1-D CNNs. • The ensemble model's performance is improved and archived state-of-the-art accuracy. Variable refrigerant flow (VRF) systems are widely-adopted air conditioning systems. When system faults occur in VRF systems, the efficiency of VRF system will drop drastically. This paper presents a single 1-D CNN model and an ensemble model with parallel 1-D CNNs for diagnosing VRF system refrigerant charge faults under heating condition. From the cleaned experiment data of a commercial VRF system, 15 features are selected as the input for the proposed model with ReliefF algorithm. After training, the diagnosis accuracy of the single 1-D CNN model and ensemble 1-D CNN models is evaluated and compared with that of BPNN model and DT model. The result shows that both single 1-D CNN and ensemble 1-D CNN model can diagnose VRF system refrigerant charge fault effectively. The fault detection is also achieved in proposed models. The average diagnosis accuracy of 9-level refrigerant charge faults of the ensemble 1-D CNN model is up to 97.4%, surpassing that of BPNN model, SVM model, DT mode and DBN model. 1-D CNN based model is utilized for VRF system fault diagnosis for the first time, which lays a foundation for the expansion of the related researches. [ABSTRACT FROM AUTHOR]

The accurate classification of Typhoon Intensity (TI) based on cloud patterns in satellite images is crucial for effective disaster warning and management. However, this task is particularly challenging due to the striking visual similarities among different sub-classes of typhoons, coupled with a long-tailed distribution of class occurrences. Existing deep learning methods often struggle with biased classification stemming from imbalanced datasets and face difficulty discerning subtle differences between categories. This paper proposes a novel solution to these challenges through a hybrid framework that integrates contrastive learning and classifier learning. Contrastive learning is employed to increase the separation of similar sub-classes within the feature space, while classifier learning aims to train a discriminative and unbiased classifier. The proposed approach is evaluated extensively on the publicly available DeepTI dataset, demonstrating enhanced performance for both prominent and less frequent classes. The model achieves the 70.88% accuracy for seven categories typhoon intensity classification. The code implementation is at https://github.com/chen-huanxin/contrastive-hybrid-network. • The proposed network combines contrastive learning with classifier learning. • The proposed network can learn the relation between samples. • Contrastive learning provides the network with more supervision. • The proposed network can learn an unbiased classifier from imbalanced dataset. [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]

• An online compressor liquid floodback fault diagnosis method is proposed for VRF system. • The proposed method fills the online liquid floodback fault diagnosis knowledge gap. • The online diagnosis results show the proposed method has extremely high accuracy. • The comparison results show that the BPNN method is better than CART method. Compressor liquid floodback not only can drop the performance of the variable refrigerant flow (VRF) system, but also may cause mechanical failure. Therefore, this is necessary to perform a timely online diagnosis of that fault. The present paper proposes an online compressor liquid floodback fault diagnosis method for VRF system based on back-propagation neural network (BPNN), which fills the online compressor liquid floodback fault diagnosis knowledge gap. The proposed method main article context as follow: Firstly, the sensors in VRF system record and save the data every three seconds to form the raw dataset. Secondly, after preprocessing the raw dataset, the correlation analysis is used to filter the data variables. Thirdly, the BPNN model is established by using back-propagation neural network algorithm. The testing dataset was used to verify the reliability of the model. The online dataset was used to test the model's online diagnostic capability, and its results were also compared and analyzed to the Classification and Regression Tree (CART) model. This result indicates that the BPNN method has a low degree of over-fitting and high reliability, and its online diagnostic accuracy is up to 99.48%. By comparing results of the method BPNN and CART, it shows that the former is superior to the latter, regardless of the diagnostic accuracy or the online diagnostic stability. [ABSTRACT FROM AUTHOR]

Malfunctions would occur in a variable refrigerant flow (VRF) system after years of operation or inappropriate maintenance, thus causing unnecessary energy waste and even occupant discomfort. This study presents a machine learning based malfunction diagnosis strategy that combines the recursive feature elimination algorithm (RFE) and the classification algorithms for the typical malfunctions of VRF system. RFE based on Random Forest (RF) model firstly serves as the feature selection process to evaluate variables importance, thus acquiring the key variables related to malfunction. Then five kinds of machine learning classification models are trained using the chosen key variables to diagnosis refrigerant leakage malfunction. By comparison, the AdaBoost.M1 (ABM) model shows the most desirable performance on the all nine malfunction severity levels. The results show that the RFR-RF based feature selection method can select the most six critical variables and the ABM model established based on the six variables achieves admirable diagnostic accuracy and AUC value for faults corresponding to nine severity levels. [ABSTRACT FROM AUTHOR]

Faulty heat pump system operating can cause excessive energy consumption and an uncomfortable indoor environment. Data-driven method is one of the most widely studied Fault Detection and Diagnosis (FDD) methods in building heat pump systems. However, the fault data is difficult to collect and label, which makes it a significant challenge to apply a data-driven method. Therefore, a novel semi-supervised FDD method that could utilize small labelled and unlabelled fault data was proposed, considering unknown fault detection in unlabelled data. The fundamental model consisted of four parts: encoder, double discriminators, generator, and bagging classifiers, and all networks were integrated to train simultaneously. Additionally, batch nuclear-norm maximization and pseudo-label were utilized to improve the model. The proposed method could realize data augmentation, pseudo-label utilization, and FDD in one end-to-end model. Normal and fault data were collected from a variable refrigerant flow heat pump system to verify this method. The proposed method was compared with several methods in previous studies. The performance indexes showed that the proposed method had averaged 65.56%, 75.50%, 82.25%, 83.30%, and 86.26% overall accuracy under each training labelled group, respectively. The unlabelled data could improve the model effectively, and the averaged detection rate of unknown fault could reach 98.00%, 98.82%, 98.88%, 98.66%, 98.94%, and 98.82%, respectively, under different numbers of unlabelled data. Additionally, the analysis of unknown fault diagnosis results indicated that faults with different severities had similar operating characteristics. • A novel integrated end-to-end semi-supervised model was proposed. • The method was verified on a Variable Refrigerant Flow Heat Pump system. • The comparison between proposed and existed methods were discussed comprehensively. • The unknown fault detection and characteristics were analysed. [ABSTRACT FROM AUTHOR]

This work proposes a multicarrier energy hub system with the objective of minimizing the economy cost and the CO2 emissions of a residential building without sacrificing the household comfort and increasing the exploitation of renewable energy in daily life. The energy hub combines the electrical grid and natural gas network, a gas boiler, a heat pump, a photovoltaic plant, and a photovoltaic/thermal (PV/T) system. In addition, to increase the overall performance of the system, a battery-based energy storage system is integrated. To evaluate the optimal capacity of each energy hub component, an optimization scheduling process and the optimization problem have been solved with the YALMIP platform in the MATLAB environment. The result showed that this advanced system not only can decrease the economic cost and CO2 emissions but also reduce the impact to electrical grid. [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 fault diagnosis of air-conditioning systems is of great significance to the energy saving of buildings. This study proposes a novel fault diagnosis approach for building energy saving based on the deep learning method which is deep belief network, and its application potential in the air conditioning fault diagnosis field is investigated. Then, a parameter optimization selection strategy is developed for model optimization. Four kinds of faults of the variable flow refrigerant system under heating mode are used to evaluate the performance of the models. The fault diagnosis results show that the deep belief network model with initial parameters can be used to diagnose the faults of the variable flow refrigerant system. Through the parameter optimization selection strategy, the fault diagnosis correct rate of the optimized model is 97.7%, which is improved by 5.05% compared with the model with initial parameters. The number of hidden layers of the deep belief network model is selected to be 2 layers. This result indicates that the fault diagnosis for variable flow refrigerant systems may not require a very deep model. Additionally, the performance of the optimized deep belief network model is compared with that of the traditional back propagation neural network, and the former is better. This finding also shows that the unsupervised restricted Boltzmann machine layer for data feature reconstruction can improve the fault diagnosis performance. [ABSTRACT FROM AUTHOR]

Highlights • A new charge fault diagnosis method for VRF system. • The temperature difference variable is introduced to improve the diagnosis effect. • Especially increases the distinction between normal and excess charge fault. Abstract VRF (Variable Refrigerant Flow) system is an efficient and energy-saving air-conditioning system. The VRF system performance is significantly affected by the quantity of refrigerant (charge). Both insufficient and excessive refrigerant charge will lead a dramatic decline of air conditioning system performance. Accordingly, it is necessary to forecast refrigerant charge in existing systems. This study proposed a charge fault diagnosis method based on expert modification C5.0 decision tree. Initially, C5.0 decision tree is used to diagnosis VRF system charge faults in nine different conditions. Furthermore, in order to reduce error rate of the decision tree, expert experience is used to modify the decision tree. And temperature differential variables are used to improve the diagnosis result. The result s of the C5.0 decision tree using temperature differential variables shows fault diagnosis error rates under expert experience guidance are 10% lower than without expert experience. Besides, this method an increased accuracy in classifies between excessive and normal charge states. [ABSTRACT FROM AUTHOR]

Frosting cleaning process for dust removal is a simple and direct way to keep heat exchangers clean, and the dust removal effectiveness is strongly influenced by surface property. An object of this paper is to investigate the frosting cleaning process on super-hydrophobic surface and to determine the interaction between the frost layer and dust particles. Frosting-defrosting process on four kinds of test samples were studied by experiments. The results showed that, compared with the clean surfaces, the frost layer structure was denser and more uniform on dusty surfaces. On the super-hydrophobic surface, as a kind of heterogeneous nucleation point, the dust particle promoted frost layer growth by 57%. During the defrosting process on dusty test samples, the melted water and dust particles were reverse osmosed the frost layer. The reverse osmosed rate increased with frosting time, and it was over 0.5 under all experimental conditions. Compared with the bare surface, super-hydrophobic surface displayed a different defrosting behavior and had a shorter defrosting time. On the super-hydrophobic surface, deposited dust particles were encased in the frost-water mixture block and flaked off the surface under gravity, and the dust removal rate was up to 94.5% in this work. Because of the reverse osmosed dust movement, the wrap effect, and the weak adhesion of super-hydrophobic surface, the dust-frost mixture is easily removed. Thus, utilizing the frosting-defrosting characteristics of super-hydrophobic surface for promoting the dust removal has a good application prospect. [ABSTRACT FROM AUTHOR]

Energy demand prediction of building heating is conducive to optimal control, fault detection and diagnosis and building intelligentization. In this study, energy demand prediction models are developed through machine learning methods, including extreme learning machine, multiple linear regression, support vector regression and backpropagation neural network. Seven different meteorological parameters, operating parameters, time and indoor temperature parameters are used as feature variables of the model. Correlation analysis method is utilized to optimize the feature sets. Moreover, this paper proposes a strategy for obtaining the thermal response time of building, which is used as the time ahead of prediction models. The prediction performances of extreme learning machine models with various hidden layer nodes are analyzed and contrasted. Actual data of building heating using a ground source heat pump system are collected and used to test the performances of the models. Results show that the thermal response time of the building is approximately 40 min. Four feature sets are obtained, and the performances of the models with feature set 4 are better. For different machine learning methods, the performances of extreme learning machine models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the extreme learning machine model with feature set 4. [ABSTRACT FROM AUTHOR]

Analysis and application for real-time operational data of buildings is important for energy management. But original data inevitably contains a number of outliers and which usually lead to a significant negative impact on performance of data-based models. In order to eliminate the influence of outliers and improve the robustness of data-based models, this study employs three methods (Boxplot, local outlier factor (LOF) and PCOut) to identify the potential outlying observations in original data set. For purpose of evaluating the outlier detection performance of these three methods, four SVR-based electricity consumption prediction models, Original-SVR, BOX-SVR, LOF-SVR and PCO-SVR, are established. And the performance indexes (RE, RMSE and RSE) of the models are compared and analyzed. The results show that the accuracy of electricity consumption prediction is improved with the help of Boxplot and LOF methods for outlier detection, but PCOut method reduces the accuracy compared with the Original-SVR model. Further study indicates that these observations which repeatedly identified as outlying by Boxplot and LOF methods are the most likely to be abnormal, and when these samples are removed from training data set, the RMSE falls to 2.76 from 6.44 and the RSE falls to 0.11 from 0.58 during testing course. [ABSTRACT FROM AUTHOR]

• Transform segmentation task from classification to regression by making models learn a level set function. • Design a shape-based loss with a convexity constraint term. • Improve segmentation performance at the boundary with weak contrast. The lens is one of the important refractive media in the eyeball. Abnormality of the nucleus or cortex in the lens can lead to ocular disorders such as cataracts and presbyopia. To achieve an accurate diagnosis, segmentation of these ocular structures from anterior segment optical coherence tomography (AS-OCT) is essential. However, weak-contrast boundaries of the object in the images present a challenge for accurate segmentation. The state-of-the-art (SOTA) methods, such as U-Net, treat segmentation as a binary classification of pixels, which cannot handle pixels on weak-contrast boundaries well. In this paper, we propose to incorporate shape prior into a deep learning framework for accurate nucleus and cortex segmentation. Specifically, we propose to learn a level set function, whose zero-level set represents the object boundary, through a convolutional neural network. Moreover, we design a novel shape-based loss function, where the shape prior knowledge can be naturally embedded into the learning procedure, leading to improvement in performance. We collect a high-quality AS-OCT image dataset with precise annotations to train our model. Abundant experiments are conducted to verify the effectiveness of the proposed framework and the novel shape-based loss. The mean Intersection over Unions (MIoUs) of the proposed method for lens nucleus and cortex segmentation are 0.946 and 0.957, and the mean Euclidean Distance (MED) measure, which can reflect the accuracy of the segmentation boundary, are 6.746 and 2.045 pixels. In addition, the proposed shape-based loss improves the SOTA models on the nucleus and cortex segmentation tasks by an average of 0.0156 and 0.0078 in the MIoU metric and 1.394 and 0.134 pixels in the MED metric. We transform the segmentation from a classification task to a regression task by making the model learn the level set function, and embed shape information in deep learning by designing loss functions. This allows the proposed method to be more efficient in the segmentation of the object with weak-contrast boundaries. We propose to incorporate shape priors into a deep learning framework for accurate nucleus and cortex segmentation from AS-OCT images. Specifically, we propose to learn a level set function, where the zero-level set represents the boundary of the target. Meanwhile, we design a novel shape-based loss function in which additional convex shape prior can be embedded in the learning process, leading to an improvement in performance. The IOUs for nucleus and cortex segmentation are 0.946 and 0.957, while the MED that reflects the accuracy of the boundary are 6.746 and 2.045 pixels. The proposed shape-based loss improves the SOTA model for nucleus and cortex segmentation by an average of 0.0156 and 0.0078 in IOU, and 1.394 and 0.134 pixels in MED. We transform segmentation from classification to regression by making the model learn a level set function, resulting in improved performance at the boundary with weak contrast. [ABSTRACT FROM AUTHOR]

• Oat protein/pullulan (Op/Pul) film and Nisin-loaded Op/Pul film were developed. • Ultrasound improved the physicochemical properties of the composite films. • Ultrasound improved the structural characteristics of the composite films. • Ultrasound improved the composite films' preservation effect on fresh strawberries. • Ultrasound has a promising effect in prepared of oat protein-based films. This study developed two novel food packaging films, oat protein/pullulan (Op/Pul) and Nisin-loaded oat protein/pullulan (Nis@Op/Pul) films. Ultrasound was introduced to improve its mechanical, structural and physicochemical properties. The Op/Pul film has lower light transmittance, water vapour and oxygen permeability (OP) and improved film uniformity than pure oat protein and pullulan film. The addition of Nisin led to a significant decrease in the composite films' transparency, moisture content, and total soluble matter (TSM). The ultrasound treatment significantly increased the elongation at break and transparency of Nis@Op/Pul film by 18.37% and 8.03% and decreased its TSM and OP by 8.33% and 2.78%, respectively, compared to the conventional method. The structure analysis shows ultrasound enhances intermolecular hydrogen bonding, reduces the crystallinity and formed a more regular, uniform surface. Moreover, the Nis@Op/Pul film prepared by ultrasound treatment could effectively delay the decay and deterioration of fresh strawberries and prolong their shelf life. [ABSTRACT FROM AUTHOR]

Aim: To analyze the changes of chronotypes in patients with depression before and after treatment, and explore the effects of different chronotypes on antidepressant treatment and the dimensions of common symptoms in patients with depression. Methods: 180 patients with depression were selected from 10 tertiary psychiatric hospitals in Zhejiang province, according to the scores of morningness-eveningness questionnaire (MEQ), the patients were divided into three groups: early-type group, middle-type group and late-type group. The 17-item Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Rating Scale anxiety Scale (HAMA), Snaith Hamilton Pleasure Scale (SHAPS), multidimensional fatigue inventory-20(MFI-20) and Pittsburgh sleep quality index (PSQI) were measured at baseline and at the end of the 2nd, 4th, 8th and 12th weeks, the variance analysis of repeated measures was used to analyze the change of each index in the study. The remission rate of depression at each time point was statistically analyzed. Results: Of the 180 patients included in the study, 26 were lost to follow-up, and 154 were finally included in the analysis. At baseline, 14.93%, 56.5% and 28.57% of the subjects were diagnosed as middle-late type, middle-late type and early-late type respectively, the total scores of Shaps and MFI-20 in the early-type group were higher than those in the late-type group and the middle-type group (p < 0.05). During the 12-week antidepressant treatment period, the time effect of PSQI, Shaps, MFI-20 and MEQ had interaction with different time groups (p < 0.05). During the treatment, the multiple symptom dimensions of depression were improved to different degrees, but the changing trend was not the same, and the recovery of the anhedonia was obviously delayed, in early-type patients, there are many symptoms such as loss of pleasure and sleep disorders. There was no significant effect on the remission rate of depression in different time type (p > 0.05). Conclusion: The disorder of chronotypes is common in patients with depression. The time effect of different time type on different symptom dimension of depression was affected, but the effect on remission rate of depression was not significant. To strengthen the management of biological chronotype rhythm in patients with depression may be of great significance in the treatment of depression. [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]

Sensor faults of air conditioning systems are harmful to optimal control strategies and system performance resulting in poor control of the indoor environment and waste of energy. In order to improve the fault detection and diagnosis (FDD) performance, this paper presents an enhanced sensor fault detection and diagnosis method based on the Satizky-Golay (SG) method and principal component analysis (PCA) method for the VRF system, namely SG-PCA method. Due to the volatility of the original data set of VRF system, the original data are smoothed using SG method at first. Then, the smoothed data are used for PCA model training and fault detection and diagnosis. In order to determine parameters of the SG method, an optimization index is proposed, which is calculated by the signal to noise ratio (SNR), the standard deviation (SD) and the self-detection efficiency. This SG-PCA method for VRF system sensor FDD is validated using field operation data of the VRF system. Various sensor faults at different fault levels are introduced. The results have showed that the SG-PCA method can significantly improve the fault detection and diagnosis performance compared to conventional PCA method. [ABSTRACT FROM AUTHOR]

This paper proposed a universal virtual variable-speed compressor power (VVCP) sensor for VRF system based on 20-coefficient model. Compressor power can be obtained by the VVCP sensor using three input parameters (frequency, condensing temperature and evaporation temperature) which are measured by system itself. The performance of the proposed VVCP sensor is evaluated using experiments data. Experimental conditions include cooling, heating and non-standard refrigerant charge levels. The result shows that the mean square percentage errors (MSPE) are 9.9% under cooling conditions and 7.96% under heating conditions. The MSPE are 9.88% at steady state and 9.75% at dynamic state when the VVCP sensor is applied under nine different refrigerant charge levels. It demonstrated that the proposed VVCP sensor can obtain compressor power under cooling, heating and non-standard refrigerant charge levels, which could be applied to do operational monitoring and fault detection and diagnosis for VRF system at low cost. [ABSTRACT FROM AUTHOR]

Variable refrigerant flow systems account for a considerable portion of energy consumption in buildings. In order to improve the energy efficiency and estimate the energy saving potentials of variable refrigerant flow systems this study proposes a data mining based method to identify and interpret the power consumption patterns and associations. Two descriptive data mining algorithms, clustering analysis and association rules mining, are used for data partitioning and association mining. The proposed method consists of four phases: data pre-processing, data partitioning, data association mining and knowledge interpretation. Experimental data collected from a tested variable refrigerant flow system in the standard psychrometer testing room are pre-processed and prepared to examine the proposed method. Three time independent influential factors: part load ratio, refrigerant charge level and cooling condition are analyzed. Results show that the method is able to help identify energy consumption patterns and extract energy consumption rules in variable refrigerant flow systems. Three distinct energy consumption patterns are identified: undercharge fault, low and high part load ratio conditions. For compressor operation frequency switch control and refrigerant undercharge patterns, the energy saving potentials could be estimated by making comparisons between energy patterns and rules in a top-down way. [ABSTRACT FROM AUTHOR]

• The influences of various common faults on the VRFs under different fault levels and load rates was analyzed. • A faults experienced database for VRFs was provided. • The fault mechanism was clarified through sensitivity analysis between the physical parameters and different faults. • A reference basis for selecting various fault features and key fault decoupling variables was provided when a multiple fault diagnosis model for VRFs is established. Due to improper installation, operation, and maintenance, many common faults inevitably occur in the air conditioning systems. However, the same fault could demonstrate different characteristics under different load rates and fault levels; different faults may present similar fault phenomena. This paper reveals the quantitative impact of faults on system performance of VRF system with common faults imposed in heating mode through fault experiments. The system parameters sensitivity study has been carried out to understand the internal impact mechanism related to system parameters. This provides a reference for selecting characteristic parameters and fault decoupling variables and developing interpretable, professional, and reliable fault detection and diagnosis models. The results show that the outdoor unit fouling fault can cause the heating capacity to drop by 73.2%, and the indoor unit fouling can cause the system power to drop by 65.5%. The coefficient of performance drop is as high as 48.6%. The sensitivity study result shows that the system load rate is an influential parameter; it needs to be considered when characteristic parameters and fault decoupling variables are selected. [ABSTRACT FROM AUTHOR]

This paper presents an entire sensor fault detection and diagnosis (FDD) for screw chiller system using the support vector data description (SVDD) algorithm. It has advantages of solving problems on describing non-linear and non-Gaussian distributed data. A distance-based D -statistic plot is employed to detect sensor faults. Based on the distance transformation in mathematical way, a new distance variation-based DV -contribution plot is proposed to diagnose the sensor fault. The screw chiller field measured data is utilized to train the SVDD model via a hybrid parameter tuning approach combined the grid search and the 10-fold cross validation. Six typical sensor faults are introduced for validation, i.e. positive and negative biases, positive and negative drifts, precision degradation and complete failure. Test results of both D -statistic and DV -contribution plots show that the SVDD-based method has good FDD results for the six sensor faults. Furthermore, the proposed DV -contribution plot shows more accurate fault diagnosis results compared with the principal component analysis (PCA)-based Q -contribution plot. [ABSTRACT FROM AUTHOR]

• Proposed an interpretable deep learning model using the attention-BiLSTM. • Four typical faults of VRF are well identified with an overall diagnosis accuracy of 97.5%. • Effective fault diagnosis under incomplete information is realized through the integrated model of different feature combinations. • Realize the quantification of diagnosis uncertainty through the ensemble model. • The model performs well on the unknown data set under different working conditions. Developing effective fault detection and diagnosis (FDD) model is of great significance to the energy efficiency and comfort of variable refrigerant flow (VRF) system. In recent FDD studies, the consideration of incomplete information and uncertainty, as well as the generalization of model and the interpretability of fault action mechanism have become a great concern. This paper accordingly proposes a fault diagnosis strategy based on attention-BiLSTM with ensemble feature sets. Three methods including Correlation Analysis, Information Entropy, and Gini Impurity are used for feature selection and ensemble feature sets combination. The proposed model can achieve interpretability of fault diagnosis and quantification of diagnosis uncertainties through attention mechanism and ensemble feature sets. A total of four typical faults under different working conditions are used to verify the accuracy and generalization of the proposed method. The results show that the ensemble attention-BiLSTM model has a fault diagnosis accuracy rate of 98.3% if the first two most likely outputs are considered, and when some sensors fail, the accuracy rate can still remain above 90%. In addition, a generalization verification idea is proposed, that is, the model is trained under specific operating conditions and tested on datasets with different operating conditions or fault levels. The results show that the average generalization of the optimized model reaches 86%. [ABSTRACT FROM AUTHOR]

• A combined generative network is built based on VAE and WGAN-GP. • The ensemble classifiers are embedded into generative network for FDD. • A detailed comparison between SMOTE and generative network method is discussed. Faults in building Heating, Ventilation, and Air-condition (HVAC) system create an uncomfortable indoor environment and cause energy waste. The data-driven method has been widely applied for Fault Detection and Diagnosis (FDD) in the complex building HVAC system. This method relies on the availability of many fault data which is difficult to collect. This makes it quite challenging to apply the data-driven methods for the FDD of the HVAC system. Thus, a novel data-driven FDD method that only utilizes small fault data collected from a Variable Refrigerant Flow air condition system has been proposed. Under different conditions, the fault and normal data are collected in an enthalpy difference laboratory to create small and imbalanced data. A generative network is developed by combining Wasserstein Generative Adversarial Network with Gradient Penalty and Variational Auto-Encoder. To improve the FDD classifier's accuracy and to train an end-to-end network model using small and imbalanced data, two ensemble classifiers are embedded into the generative network. The dataset includes normal and fault data have been applied to train the modified generative network, and two ensemble classifiers are used to detect and diagnose the fault, respectively. The performance indexes show that the proposed method is much better than the SMOTE-based methods in almost all training groups. Besides, the comparison between the proposed method and generative network with a single classifier indicates that the ensemble classifiers can improve the F1-score of fault detection and the accuracy of fault diagnosis. [ABSTRACT FROM AUTHOR]

Detecting the faults at the incipient stage is important for keeping chiller systems healthy and saving energy and maintenance cost. Traditional principle component analysis (PCA) and support vector data description (SVDD) methods are insensitive to two common faults, condenser fouling (CdF) and refrigerant leakage (RfL). To improve the fault detection performance, this study proposed a PCA-R-SVDD based method. Instead of principle component subspace (PCs), it develops a SVDD model in the residual subspace (Rs) using the PCA modeling residual data. The SVDD based distance based monitoring statistic was used for fault detection. The proposed method shows significant improvement comparing with the traditional methods due to the better fault data distribution and tighter monitoring statistic. It is sensitive to six common faults. At least 50% of the fault data can be correctly detected even at the least severe fault level. Centrifugal chiller experimental data from the ASHRAE Research Project 1043 (RP-1043) was used to evaluate the methods. [ABSTRACT FROM AUTHOR]