With the atmospheric concentration of CO2 increasing, it is importanto know how this will affect crop growth. The objective of the study was to determine the effect of elevated CO2 on big bluestem (Andropogon gerardii Vitman) growing in a tallgrass prairie on Tully silty clay loam (fine, mixed, mesic Pachic Argiustoll) kept a high water level (field capacity) or a low water level (half field capacity). Sixteen cylindrical plastic chambers were placed on the prairie to maintain the two levels of CO2 (mean ± SD: 337 ± 32 and 658 ± 81 µmol mol‐1) over a full growing season. Soil‐water content was measured weekly with a neutron probe. Photosynthesis, transpiration, stonmtal resistance, and intercellular CO2 concentration were determined with a portable leaf photosynthetic system. Canopy temperature was monitored with an infrared thermometer. Elevated (doubled) CO2 reduced transpiration rate of big binestem by 25 and 35% under the high‐ and low‐water treatments, respectively. Under both watering regimes, stomatal resistance was greater by ≈1.6 s cm‐1 with doubled CO2 than with ambient CO2. Plants grown with doubled CO2 at high‐ and low‐water levels had warmer canopy temperatures (average 1.15 and 0.70 °C warmer, respectively) than plants grown ambient CO2. Carbon‐dioxide concentration did not affect the rate of photosynthesis, even though intercellular CO2 concentration was increased under high CO2. Elevated CO2 did not increase the height of plants grown at the high water level, but it did increase the height at the low water level by an average of 9 cm.
Accurate prediction of photovoltaic power is of great significance to the safe operation of power grids. In order to improve the prediction accuracy, a similar day clustering convolutional neural network (CNN)–informer model was proposed to predict the photovoltaic power. Based on correlation analysis, it was determined that global horizontal radiation was the meteorological factor that had the greatest impact on photovoltaic power, and the dataset was divided into four categories according to the correlation between meteorological factors and photovoltaic power fluctuation characteristics; then, a CNN was used to extract the feature information and trends of different subsets, and the features output by CNN were fused and input into the informer model. The informer model was used to establish the temporal feature relationship between historical data, and the final photovoltaic power generation power prediction result was obtained. The experimental results show that the proposed CNN–informer prediction method has high accuracy and stability in photovoltaic power generation prediction and outperforms other deep learning methods.
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