(1) Background: As environmental issues gain more attention, switching from conventional energy has become a recurring theme. This has led to the widespread development of photovoltaic (PV) power generation systems. PV supports, which support PV power generation systems, are extremely vulnerable to wind loads. For sustainable development, corresponding wind load research should be carried out on PV supports. (2) Methods: First, the effects of several variables, including the body-type coefficient, wind direction angle, and panel inclination angle, on the wind loads of PV supports are discussed. Secondly, the wind-induced vibration of PV supports is studied. Finally, the calculation method of the wind load on PV supports is summarized. (3) Conclusions: According to the particularity of the PV support structure, the impact of different factors on the PV support’s wind load should be comprehensively considered, and a more accurate method should be adopted to evaluate and calculate the wind load to lessen the damage that a PV support’s wind-induced vibration causes, improve the force safety of PV supports, and thereby enhance the power generation efficiency of PV systems.

Wind load is one of the main factors of plastic greenhouse collapse. To solve the dynamic response problem of greenhouses under wind load and determine the dangerous section of a skeleton structure, the investigated lump method is presented for the dynamic response analysis of a plastic greenhouse, considering pulsating wind on the basis of Timoshenko beam theory. First, the investigated lump is designed according to the Timoshenko beam microbody concept. On the basis of Timoshenko beam theory, the governing equations of the skeleton structure of the greenhouse are derived, and the realization process of the algorithm is also provided. Second, the accuracy and effectiveness of the proposed numerical method are verified by an example in which the bending wave of a variable cross section beam with free ends propagates. Finally, the dynamic response of the steel skeletons of plastic greenhouses is analyzed under the effect of the simulation wind speed, and the spatial distribution of the maximum node displacement and the section maximum stress of the steel skeleton are obtained. Computational results show that the displacement peak is near the top of the plastic greenhouse. The most dangerous section of the top chord in the steel skeleton is near the leeward bottom, which has a maximum stress of 219.4 MPa, and the most dangerous section of the bottom chord is near the 1 m height on the leeward side of the plastic greenhouse, which has a maximum stress of 248.5 MPa. Bending stress is the main factor of the rapid increase of stress at the bottom of the skeleton. The maximum node displacement and cross‐sectional stress caused by fluctuating wind loads are higher than those caused by average wind loads. The fluctuating wind load should be considered in the wind‐induced response analyses of plastic greenhouses.

A plant factory is typically considered to be an exceedingly advanced product management system characterized by higher crop yields and better quality control. The pH value of the nutrient solution is crucial for determining the health and productivity of crops. However, the nutrient solution process exhibits inherent complexity, such as parameters uncertainty, multi-disturbances, and strong nonlinearity. Therefore, the traditional control method cannot meet the necessary requirements. The main objective of this paper is to address the issues of parameter uncertainty, strong nonlinearity, and multiple disturbances in the regulation process of the nutrient solution while achieving accurate control of the nutrient solution pH in a plant factory. This is performed so that a dynamic model of a nutrient solution for pH is developed and a nonlinear adaptive controller is presented, which comprises a linear adaptive generalized predictive controller, a nonlinear adaptive generalized predictive controller, and a switching mechanism. The parameters of the controller are adjusted by generalized predictive control (GPC) laws. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the unmodeled dynamics to depress the influence of nonlinearity on the system. The experiments show that the mean errors and standard errors for gain-scheduling the proportional-integral-derivative (PID) control strategy are 0.1388 and 0.4784, respectively. The mean errors and standard errors for the nonlinear adaptive controller are 0.1046 and 0.3009, respectively. Simulation results indicate that the presented method can acquire a better control effect in the case of various complex situations. Therefore, by achieving precise control of the pH value, it is possible to provide a suitable growth environment for crops, promoting healthy crop growth and increasing crop yield.