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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]

• 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]

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]

Abstract: This paper presents a novel thermoelectric radiant air-conditioning (TE-RAC) system, which employs the thermoelectric modules as radiant panels instead of conventional hydronic panels. The novel radiant system brings a lot of benefits including Freon free, convenient installation, no complex water distribution pipes, quiet and reliable operation, etc. Switching between cooling and heating modes can be easily achieved by reversing the input current. The purpose of this study is to investigate the feasibility and performance of the novel TE-RAC system, including its cooling and heating performances, condensation risk, dynamic thermal response and inertia, and cost-effectiveness. A case study of the TE-RAC system is presented to compare the TE-RAC system with conventional air-conditioning (AC) systems and radiant air-conditioning (RAC) systems in terms of cooling and heating capacities, energy consumption and operation reliability. It is found that the COP of the TE-RAC system could be comparable to conventional RAC systems and AC systems, besides many other benefits in life-cycle performances. [Copyright &y& Elsevier]

Variable refrigerant flow (VRF) systems have gained much attention and been widely used in commercial and residential buildings benefitting from their competitive advantages. However, after long-term operation in a complex environment, various faults may occur in the VRF systems, resulting in failure to meet users comfort requirements and even unnecessary increase in energy consumption. This paper proposes a simulated annealing wrapped generic ensemble fault diagnosis strategy for typical faults of VRF systems, such as refrigerant charge amount (RCA) faults, valve faults, and compressor liquid return (LF) faults. The simulated annealing algorithm based on random forest (SA-RF) is first utilized to perform feature selection process on the three kinds of fault datasets to select the optimal variables that can well characterize the fault states, which can improve the modeling efficiency while reducing the data dimensionality. Then five component learners and the proposed ensemble model based on them are established adopting the optimal variables as input variables. Through visualizing the error evolution and margin of the boosting models built in the first stage of the integration process, it was found that the boosting models can effectively avoid overfitting and most samples are correctly classified with high confidence. By comparing with the five component learners, it is concluded that the boosting strategy in the first stage can improve the diagnostic performance of the models, and the weighted voting integration strategy in the second stage can further improve the diagnostic performance of the model. The final ensemble model can effectively compensate for the deficiencies of each component learners and its diagnostic accuracy for the three fault data sets is as high as 95.37%, 99.36% and 98.3%, respectively, indicating that the model can be applied to diagnose the three types of faults in VRF system at the same time, showing a high versatility. [ABSTRACT FROM AUTHOR]

• We propose a comparison study of basic data-driven fault diagnosis for VRF system. • The performance under single fault and multiple faults is tested and compared. • DNN is preferred for multiple faults, and SVM is selected for single faults. • All method has lower HR for F5 and F6 than the rest in the multiple fault diagnosis. • We came up with suggestions based on comparative analysis for subsequent research. HVAC systems occupy a large part of the building's energy consumption. The development of fault detection and diagnosis (FDD) techniques for HVAC system is becoming increasingly essential for building energy saving. The present paper proposes a comparison study on the basic data-driven methods for variable refrigerant flow (VRF) system fault diagnosis. And five widely used data-driven methods were analyzed, which were decision tree (DT), support vector machines (SVM), clustering (CL), shallow neural networks (SNN), and deep neural networks (DNN). The six common types of fault data in VRF system and three evaluation indexes were used to compare the performance of the proposed five FDD methods in single fault and multiple faults. Results indicate that the single fault diagnosis performance of all methods is better than multiple fault diagnosis. The performances of DNN, SNN, and SVM are better than CART and CL no matter single fault or multiple fault diagnosis. Among them, we believe that the SVM method is preferred for single fault diagnosis and the DNN method is preferred for multiple fault diagnosis. And the performance of method CL is the worst, especially in the case of multiple fault classification, the accuracy is only about 48%. The study results are dedicated to providing a reference for subsequent research in FDD of VRF system. Some important remarks are finally concluded in this paper. [ABSTRACT FROM AUTHOR]

• The potentials of deep reinforcement learning (DRL) techniques are investigated. • Three commonly-used DRL techniques, i.e. A3C, DDPG and RDPG are utilized for building energy consumption forecasting. • The prediction performances of six popular predictive algorithms are studied. • DDPG and RDPG can evidently enhance prediction performance while accounting for more computation time. • A3C doesn't show advantage in forecasting building energy consumption. Reliable and accurate building energy consumption prediction is becoming increasingly pivotal in building energy management. Currently, data-driven approach has shown promising performances and gained lots of research attention due to its efficiency and flexibility. As a combination of reinforcement learning and deep learning, deep reinforcement learning (DRL) techniques are expected to solve nonlinear and complex issues. However, very little is known about DRL techniques in forecasting building energy consumption. Therefore, this paper presents a case study of an office building using three commonly-used DRL techniques to forecast building energy consumption, namely Asynchronous Advantage Actor-Critic (A3C), Deep Deterministic Policy Gradient (DDPG) and Recurrent Deterministic Policy Gradient (RDPG). The objective is to investigate the potential of DRL techniques in building energy consumption prediction field. A comprehensive comparison between DRL models and common supervised models is also provided. The results demonstrate that the proposed DDPG and RDPG models have obvious advantages in forecasting building energy consumption compared to common supervised models, while accounting for more computation time for model training. Their prediction performances measured by mean absolute error (MAE) can be improved by 16%-24% for single-step ahead prediction, and 19%-32% for multi-step ahead prediction. The results also indicate that A3C performs poor prediction accuracy and shows much slower convergence speed than DDPG and RDPG. However, A3C is still the most efficient technique among these three DRL methods. The findings are enlightening and the proposed DRL methodologies can be positively extended to other prediction problems, e.g., wind speed prediction and electricity load prediction. [ABSTRACT FROM AUTHOR]