Due to global energy depletion,solar energy technology has been widely used in the world.The output power of the solar energy systems is affected by solar radiation.Accurate short-term forecasting of solar radiation c...Due to global energy depletion,solar energy technology has been widely used in the world.The output power of the solar energy systems is affected by solar radiation.Accurate short-term forecasting of solar radiation can ensure the safety of photovoltaic grids and improve the utilization efficiency of the solar energy systems.In the study,a new decomposition-boosting model using artificial intelligence is proposed to realize the solar radiation multi-step prediction.The proposed model includes four parts:signal decomposition(EWT),neural network(NARX),Adaboost and ARIMA.Three real solar radiation datasets from Changde,China were used to validate the efficiency of the proposed model.To verify the robustness of the multi-step prediction model,this experiment compared nine models and made 1,3,and 5 steps ahead predictions for the time series.It is verified that the proposed model has the best performance among all models.展开更多
Two separate experiments were conducted in bell pepper （Capsicum annum L.） in order to evaluate the effects of temperature and radiation on fruit yield. The results of the temperature experiment were integrated into...Two separate experiments were conducted in bell pepper （Capsicum annum L.） in order to evaluate the effects of temperature and radiation on fruit yield. The results of the temperature experiment were integrated into the radiation experiment to give an overall empirical model for potential pepper fruit yield grown in greenhouse. In the temperature experiment, pepper plants were planted during the summer time of Israel in the Arava region in a commercial, one hectare greenhouse, equipped with a cooling wet-mat system. Eleven plots were assigned along the 80 m down the row from the wet mat. Air seasonal temperatures were affected by the distance from the wet-mat and linearly increased at the rate of 0.036 ℃/m, while relative humidity was not affected. Fruit yield dropped from 19.4 kg/m at a distance of 20 m, to 13.1 kg/m2 at 80 m away from the wet-mat, respectively. Yield regression decreased linearly with increased temperature at -11%/℃. In the radiation experiment, during the summer time of Israel in the Western Negev region, three sweet pepper varieties were grown under six radiation treatments, which accumulated to the following relative global radiation fractions （lint/lout）： 0.72, 0.61, 0.46, 0.38, 0.32 and 0.21 from outside radiation. The three varieties did not differ in their response to radiation. The seasonal temperature normalized yield response to radiation quantity at 21 ℃ （Y21） yielded a linear regression formula with a slope of 7.6 × 10^-3 kg/m^2/MJ. The multiplicative model of temperature and radiation on fruit yield was found to predict well the potential fruit yield for various locations and seasons in Israel.展开更多
基金Project(2020TJ-Q06)supported by Hunan Provincial Science&Technology Talent Support,ChinaProject(KQ1707017)supported by the Changsha Science&Technology,ChinaProject(2019CX005)supported by the Innovation Driven Project of the Central South University,China。
文摘Due to global energy depletion,solar energy technology has been widely used in the world.The output power of the solar energy systems is affected by solar radiation.Accurate short-term forecasting of solar radiation can ensure the safety of photovoltaic grids and improve the utilization efficiency of the solar energy systems.In the study,a new decomposition-boosting model using artificial intelligence is proposed to realize the solar radiation multi-step prediction.The proposed model includes four parts:signal decomposition(EWT),neural network(NARX),Adaboost and ARIMA.Three real solar radiation datasets from Changde,China were used to validate the efficiency of the proposed model.To verify the robustness of the multi-step prediction model,this experiment compared nine models and made 1,3,and 5 steps ahead predictions for the time series.It is verified that the proposed model has the best performance among all models.
文摘Two separate experiments were conducted in bell pepper （Capsicum annum L.） in order to evaluate the effects of temperature and radiation on fruit yield. The results of the temperature experiment were integrated into the radiation experiment to give an overall empirical model for potential pepper fruit yield grown in greenhouse. In the temperature experiment, pepper plants were planted during the summer time of Israel in the Arava region in a commercial, one hectare greenhouse, equipped with a cooling wet-mat system. Eleven plots were assigned along the 80 m down the row from the wet mat. Air seasonal temperatures were affected by the distance from the wet-mat and linearly increased at the rate of 0.036 ℃/m, while relative humidity was not affected. Fruit yield dropped from 19.4 kg/m at a distance of 20 m, to 13.1 kg/m2 at 80 m away from the wet-mat, respectively. Yield regression decreased linearly with increased temperature at -11%/℃. In the radiation experiment, during the summer time of Israel in the Western Negev region, three sweet pepper varieties were grown under six radiation treatments, which accumulated to the following relative global radiation fractions （lint/lout）： 0.72, 0.61, 0.46, 0.38, 0.32 and 0.21 from outside radiation. The three varieties did not differ in their response to radiation. The seasonal temperature normalized yield response to radiation quantity at 21 ℃ （Y21） yielded a linear regression formula with a slope of 7.6 × 10^-3 kg/m^2/MJ. The multiplicative model of temperature and radiation on fruit yield was found to predict well the potential fruit yield for various locations and seasons in Israel.
