| 1. |
- Chen, Zhelun, et al.
(författare)
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A review of data-driven fault detection and diagnostics for building HVAC systems
- 2023
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 339
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Forskningsöversikt (refereegranskat)abstract
- With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD.
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| 3. |
- Lin, Baojun, et al.
(författare)
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Balancing the pre-aggregation and crystallization kinetics enables high efficiency slot-die coated organic solar cells with reduced non-radiative recombination losses
- 2020
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Ingår i: Energy & Environmental Science. - : Royal Society of Chemistry (RSC). - 1754-5692 .- 1754-5706. ; 13:8, s. 2467-2479
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Tidskriftsartikel (refereegranskat)abstract
- Slot-die coating being compatible with the roll-to-roll technique has been regarded as a promising tool for upscaling the manufacturing of organic solar cells (OSCs). However, there has been a significant gap between the efficiencies of the state-of-the-art spin-coated devices and the scalable processed devices. The active layer morphology is crucial to achieve high efficiency in OSCs, which depends on the conditions of film fabrication. To figure out and optimize the slot-die coating process, a deeper understanding of the film formation kinetics is important. Herein, in situ measurements of the slot-die coating process based on the PM7:IT4F system are demonstrated to illustrate the aggregation and crystallization evolution at various die temperatures and substrate temperatures. OSCs with a high power conversion efficiency of 13.2% are achieved at 60 degrees C die temperature/60 degrees C substrate temperature due to the improved exciton dissociation, charge transport and suppressed non-radiative charge recombination. The optimized morphology is attributed to the balanced polymer pre-aggregation and small molecule crystallization kinetics. The unsuitable die temperature leads to overlarge phase separation and consequently inefficient exciton dissociation while the improper substrate temperature results in weak crystallization and the following shrunken carrier lifetime with strong non-radiative combination. This work provides fundamental understanding on the correlations among processing methodology, solution pre-aggregation, morphology formation kinetics, device physics and device performance and affords guidance for device optimization in scalable manufacturing.
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