Browsing by Author "Zhang, Meng"

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    Enhanced performance of CsPbIBr2 perovskite solar cell by modified zinc oxide nanorods array with [6,6]‐Phenyl C61 butyric acid
    (Wiley, 2023-04-12) Yang, Jien; Zhang, Meng; Zhang, Qiong; Qin, Chaochao; Qin, Ruiping; Jain, Sagar M.; Liu, Hairui
    Although Metal oxide ZnO is widely used as electron transport layers in all-inorganic PSCs due to high electron mobility, high transmittance, and simple preparation processing, the surface defects of ZnO suppress the quality of perovskite film and inhibit the solar cells’ performance. In this work, [6,6]-Phenyl C61 butyric acid (PCBA) modified zinc oxide nanorods (ZnO NRs) is employed as electron transport layer in perovskite solar cells. The resulting perovskite film coated on the zinc oxide nanorods has better crystallinity and uniformity, facilitating charge carrier transportation, reducing recombination losses, and ultimately improving the cells’ performance. The perovskite solar cell with the device configuration of ITO/ZnO nanorods/PCBA/CsPbIBr2/Spiro-OMeTAD/Au delivers a high short circuit current density of 11.83 mA cm-2 and power conversion efficiency of 12.05 %.
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    Wind power forecasting considering data privacy protection: a federated deep reinforcement learning approach
    (Elsevier, 2022-11-16) Li, Yang; Wang, Ruinong; Li, Yuanzheng; Zhang, Meng; Long, Chao
    In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving and data islands problem. To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL). Firstly, this paper uses the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to improve prediction accuracy. Secondly, we integrate the DDPG forecasting model into the framework of federated learning. The designed FedDRL can obtain an accurate prediction model in a decentralized way by sharing model parameters instead of sharing private data which can avoid sensitive privacy issues. The simulation results show that the proposed FedDRL outperforms the traditional prediction methods in terms of forecasting accuracy. More importantly, while ensuring the forecasting performance, FedDRL can effectively protect the data privacy and relieve the communication pressure compared with the traditional centralized forecasting method. In addition, a simulation with different federated learning parameters is conducted to confirm the robustness of the proposed scheme.