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  • Open Access


    Research on WNN Greenhouse Temperature Prediction Method Based on GA

    Wenbin Dai1, Lina Wang1,2,*, Binrui Wang1, Xiaohong Cui1, Xue Li1

    Phyton-International Journal of Experimental Botany, Vol.91, No.10, pp. 2283-2296, 2022, DOI:10.32604/phyton.2022.021096

    Abstract Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data to model greenhouses, but there is a widening gap in the application of different neural networks. This paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm (GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis… More >

  • Open Access


    Tea Plantation Frost Damage Early Warning Using a Two-Fold Method for Temperature Prediction

    Zhengyu Wu1, Kaiqiang Li1, Lin Yuan2, Jingcheng Zhang1, Xianfeng Zhou1,*, Dongmei Chen1,*, Kaihua Wei1

    Phyton-International Journal of Experimental Botany, Vol.91, No.10, pp. 2269-2282, 2022, DOI:10.32604/phyton.2022.022607

    Abstract As the source and main producing area of tea in the world, China has formed unique tea culture, and achieved remarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frost damage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden is very important for tea plantation management and economic values. Aiming at the problems existing in current meteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological… More >

  • Open Access


    A TMA-Seq2seq Network for Multi-Factor Time Series Sea Surface Temperature Prediction

    Qi He1, Wenlong Li1, Zengzhou Hao2, Guohua Liu3, Dongmei Huang1, Wei Song1,*, Huifang Xu4, Fayez Alqahtani5, Jeong-Uk Kim6

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 51-67, 2022, DOI:10.32604/cmc.2022.026771

    Abstract Sea surface temperature (SST) is closely related to global climate change, ocean ecosystem, and ocean disaster. Accurate prediction of SST is an urgent and challenging task. With a vast amount of ocean monitoring data are continually collected, data-driven methods for SST time-series prediction show promising results. However, they are limited by neglecting complex interactions between SST and other ocean environmental factors, such as air temperature and wind speed. This paper uses multi-factor time series SST data to propose a sequence-to-sequence network with two-module attention (TMA-Seq2seq) for long-term time series SST prediction. Specifically, TMA-Seq2seq is an… More >

  • Open Access


    Investigation of Inverter Temperature Prediction Model in Wind Farm Based on SCADA Data

    Qihui Ling1,2,*, Wei Zhang2, Qiancheng Zhao1,2, Juchuan Dai1,2

    Energy Engineering, Vol.119, No.1, pp. 287-300, 2022, DOI:10.32604/EE.2022.014718

    Abstract The inverter is one of the key components of wind turbine, and it is a complex circuit composed of a series of components such as a variety of electronic components and power devices. Therefore, it is difficult to accurately identify the operation states of inverter and some problems regarding its own circuit, especially in the early stages of failure. However, if the inverter temperature prediction model can be established, the early states can be identified through the judgment of the output temperature. Accordingly, considering whether the inverter heats up normally from the perspective of heat… More >

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