Faults that impair performance can occur in a heat source system because it comprises various devices and has complex controls. This article presents a novel method for fault detection and diagnosis (FDD). This study focused on a real system with a water thermal storage tank. First, system behaviors in response to faults were determined using a detailed system simulation. Then, a fault database was generated using the simulation results with fault labels. We preprocessed the database and converted the data into images. Then, convolutional neural networks (CNNs) were trained using the database, and the trained CNNs were used for diagnosing real data. The accuracy of the CNNs was 98.7% in training, and real data were diagnosed with probabilities. We analyzed the real data, where the probability indicated the likely presence of a fault and reviewed how the real data were similar to the fault assumed in the simulation. We concluded that the proposed FDD method will help in analyzing real data, as it indicates faults emerging in the real data with probability, whereas conventional data analysis requires checking the data using expert knowledge.

In this paper, we present a performance-based approach to building configuration design to improve the urban ventilation potential at the conceptual design stage, and we demonstrate its application through a case study. The target performance optimized was the ventilation potential of a district, including a region of interest at a spatial scale of hundreds of meters. To estimate this performance, we used computational fluid dynamics (CFD), coupled with an evolutionary algorithm, to optimize the design alternatives to produce the building configuration most suitable for a given set of site conditions. Three calculation components must be assembled for a CFD-based design optimization: an optimizer, a geometry/mesh generator, and a CFD solver. To provide links between the calculation components, we utilized an in-house parametric design program. A case study was conducted to test the applicability of the proposed design method to identify the optimal solutions that minimize adverse effects on the ventilation potential of the surrounding area. For a configuration of buildings in a dense urban area, the proposed design method successfully improved the design alternatives. The results show that the urban ventilation potential in the case of the optimized building configuration is 16% greater than that of the initial building configuration.