Greenhouse is one kind of protected cultivation facility with plastic film as the covering material and bamboo or steel frame as the supporting structure. Solar radiation is one of the most important influencing factors on the thermal energy environment inside the greenhouses. Only the greenhouse effect without heating can make the internal temperature higher than the outside in the cold regions. The solar radiation received by a greenhouse is strongly related to some parameters, such as geographical location, climatic conditions, orientation, and shape. Therefore, it is very necessary to optimize the shape, orientation, and size of greenhouses, in order to maximize the use of winter solar radiation and then effectively reduce winter heating requirements and operating costs. The optimal shape and orientation of greenhouses can vary slightly in the height of the crop canopy, such as fruit, leafy vegetables, and flowers. However, it is still lacking to consider the canopy height of the crops in the greenhouse structure, according to the capture of solar radiation on the lighting surfaces. In this study, a novel model was developed to optimize the ridge-shape greenhouse using canopy height and constant volume constraints. The amount of solar radiation was also maximized to capture the lighting surface and the effective planting area. A systematic analysis was implemented to explore the influence of the greenhouse height ratio and azimuth angle on solar radiation capture under the constant internal energy demand. The optimal combination of parameters was achieved in the different ridge ratios and regions. More light was harvested and converted at the expense of the greenhouse into heat stored in the greenhouse, thus reducing night-time heating costs. A comparative analysis was also made to verify the calculated and measured values of the solar radiation model. The results show that the solar radiation captured by the greenhouses was positively correlated with the height and ridge ratio. The difference in solar radiation with different ridge ratios decreased with the decrease in height ratio. The amount of solar radiation captured inside the greenhouse increased with the decrease of regional latitude at the same height ratio. Most solar radiation showed a decreasing and then increasing trend with the increase of azimuth angle. The preferred height ratios in different regions were related only to the height of the crop canopy. The higher the latitude under the same conditions was, the greater the ridge ratio and canopy height were. The greater angle of azimuth was preferably obtained by the greenhouse deflected with respect to the north-south direction. This finding can improve the thermal insulation and heat storage with heating cost savings in the greenhouse. A theoretical basis can also offer to advance the overall planning and development of greenhouses.

In order to solve the immaturity of decision-making methods in the regulation of winter heating in greenhouses, this study proposed a solution to the problem of greenhouse winter heating regulation using a dynamic programming algorithm. A mathematical model that included indoor environmental state variables, optimization decision variables, and outdoor random variables was established. The temperature is kept close to the expected value and the energy consumption is low. The model predicts the control solution by considering the cost function within the next 10 steps. The two-stage planning method was used to optimize the state of each moment step by step. The temperature control strategy model was obtained by training the relationship between indoor temperature, outdoor temperature, and heating time after optimization using a regression algorithm. Based on a typical Internet of Things (IoT) structure, the greenhouse control system was designed to regulate the optimal control according to the feedback of the current environment. Through testing and verification, the optimized control method could stabilize the temperature near the target value. Compared to the threshold control (threshold interval of 2.0°C) under similar weather conditions, the optimized control method reduced the temperature fluctuation range by 0.9°C and saved 7.83 kW·h of electricity, which is about 14.56% of the total experimental electricity consumption. This shows that the dynamic programming method is feasible for environmental regulation in actual greenhouse production, and further research can be expanded in terms of decision variables and policy models to achieve a more comprehensive, scientific, and precise regulation.