Day-ahead forecasting of solar radiation is essential for grid balancing, real-time unit dispatching, scheduling and trading in the solar energy utilization system. In order to provide reliable forecasts of solar radiation, a novel hybrid model is proposed in this study. The hybrid model consists of two modules: a mesoscale numerical weather prediction model (WRF: Weather Research and Forecasting) and Kalman filter. However, the Kalman filter is less likely to predict sudden changes in the forecasting errors. To address this shortcoming, we develop a new framework to implement a Kalman filter based on the clearness index. The performance of this hybrid model is evaluated using a one-year dataset of solar radiation taken from a photovoltaic plant located at Maizuru, Japan and Qinghai, China, respectively. The numerical results reveal that the proposed hybrid model performs much better in comparison with the WRF-alone forecasts under different sky conditions. In particular, in the case of clear sky conditions, the hybrid model can improve the forecasting accuracy by 95.7% and 90.9% in mean bias error (MBE), and 42.2% and 26.8% in root mean square error (RMSE) for Maizuru and Qinghai sites, respectively.
Hilly and mountainous areas are weak places for the development of agricultural mechanization in China. The way to improve the utilization rate of small agricultural machinery widely used in hilly and mountainous areas is of positive significance for optimizing resource allocation efficiency of agricultural production and ensuring food security supply. Taking microtillers as a representative tool, this study systematically analyzed the main factors affecting the utilization rate of small agricultural machines and its influencing mechanism. Then, based on the survey data of 4905 farmers in 100 counties in 10 hilly and mountainous provinces of China, empirical analysis was carried out by some econometric models, such as censored regression and the mediating effect model. Results show the following.: (1) Among farmers in hilly and mountainous areas, the average use time of each microtiller is 218.41 h per year. (2) Age, social identity, terrain conditions, crop types, land area, the number of microtillers, the number of large tractors, and the machinery purchase subsidy policy are the significant factors affecting the utilization rate of microtillers. (3) The increase of cultivated land area not only directly improves the utilization rate of microtillers, but also indirectly improves the utilization rate of microtillers due to the increase in quantity.
Agricultural machinery maintenance skill training is conducive to improving the fault diagnosis and maintenance levels of agricultural machinery for agricultural socialized service providers and plays an important role in providing stable and reliable agricultural machinery operation services. This paper aims to study whether maintenance skill training gives agricultural socialized service providers more advantages than untrained providers, exploring the relationship between maintenance skill training and agricultural machinery service area. Based on a survey of 4905 farmers from 10 provinces in China, an empirical analysis was carried out using a fixed effect model and a propensity score matching method. The results showed the following: First, maintenance skill training had a significant positive impact on agricultural machinery operation service area, including 10.426 ha of machinery tilling service area and 8.524 ha of machinery harvesting service area. Second, since maintenance skill training gave agricultural socialized service providers more advantages in agricultural machinery operation services and enabled them to obtain more orders, it had an indirect positive impact on the quantity of demand for large- and middle-sized agricultural machinery.
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