Greenhouses are high energy-consuming and anti-seasonal production facilities. In some cases, energy consumption in greenhouses accounts for 50% of the cost of greenhouse production. The high energy consumption has become a major factor hindering the development of greenhouses. In order to improve the energy efficiency of the greenhouse, it is important to predict its energy consumption. In this study, the energy consumption mathematical model of a Venlo greenhouse is established based on the principle of energy conservation. Three optimization algorithms are used to identify the parameters which are difficult to determine in the energy consumption model. In order to examine the accuracy of the model, some verifications are made. The goal of achieving high yield, high quality and high efficiency production is a problem in the study of greenhouse environment control. Combining the prediction model of greenhouse energy consumption with the relatively accurate weather forecast data for the next week, the energy consumption of greenhouse under different weather conditions is predicted. Taking the minimum energy consumption as the objective function, the indoor daily average temperatures of 7 days are optimized to provide the theoretical reference for the decision-making of heating in the greenhouse. The results show that the optimized average daily temperatures save 9% of the energy cost during a cold wave.

The commonly used greenhouse crop yield prediction models today have their specific application scenarios, which may not ensure the accuracy of the results if the greenhouse environment changes. This greatly restricts their use in the greenhouse environment. To solve this problem, two widely used tomato growth models were compared in the study: TOMGRO and Vanthoor, and then an integrated model was obtained. Through the extended Fourier amplitude sensitivity test (EFAST), the model parameters were divided into three categories: optimized, fixed and ignored. In addition, Bayesian optimization was used as an optimization algorithm, through which the parameters applicable to the greenhouse can be optimized based on the greenhouse data. Compared with TOMGRO and Vanthoor, the output of the integrated model was more reasonable and universal, and the RMSE in the integrated model was 2.5974 while that in TOMGRO and Vanthoor both were over 17, reflecting the fact that the model output was closer to the actual value. According to the verification results of four-year greenhouse data, the model had high performance in predicting yield.

Light is considered the most important environmental factor for greenhouse crops, and lack of lighting limits the productivity of greenhouses. Many studies have focused on improving the energy efficiency of lighting in agriculture. In this study, LED lights were mounted in the top, middle and bottom of the plant canopy to provide additional photosynthetically active radiation. Assuming that 100% of the predicted outdoor radiation is available, the canopy light distribution model was used to determine the relationship between the light energy consumption and the optimal yield. Decision-making on lighting scheduling may be considered as a constrained optimisation, and the status of the LEDs in a period acts as the decision variable. Besides, there are two objective functions: one is the energy consumed by the LED device; the other is the expected yield calculated by the crop model of tomatoes. The optimal solution of the multi-objective optimisation is not unique but corresponds to an infinite number of yield/energy combinations. Farmers can select these combinations according to additional restrictions such as energy efficiency ratio or photoperiod. The yield of our strategy was increased by 12.3% under the constraint of equal energy consumption compared to the greenhouse threshold control strategy. Given the same output as the constraint, the energy consumption was reduced by 30.1%.

As essential environmental parameters in the greenhouse, appropriate light and CO₂ will improve agricultural productivity and quality. Although many related studies have been carried out on the intelligent regulation of these environmental factors, the regulation of light and CO₂ is usually controlled separately, and energy consumption is rarely considered. This paper proposed a coordinated control strategy for greenhouse light and CO₂ based on the multi-objective optimization model. Firstly, the experiments on the net photosynthetic rate of blueberry under different temperatures, photon flux density, and CO₂ concentration nesting were carried out to establish a blueberry net photosynthetic rate prediction model based on Support Vector Regression (SVR). Secondly, a model for calculating the energy cost of both light and CO₂ was constructed. Thirdly, taking the maximum net photosynthetic rate and the minimum energy cost as the objective functions, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was leveraged to obtain the Pareto optimal solutions of the target regulation values of light and CO₂ concentration in different temperature ranges. Then, the optimal values were selected based on two different strategies. Finally, the multi-objective optimal control strategy proposed in this paper was compared with both the classical threshold control strategy and the Gaussian curvature maximization control strategy. The results indicated that the strategy which prioritized energy saving could reduce the energy cost by about 22.33% and 19.08%, respectively, under the premise that the net photosynthetic rate was consistent. Meanwhile, the strategy that prioritized production efficiency could increase the net photosynthetic rate by about 8.40% and 4.42%, respectively, with the same energy cost. In conclusion, the proposed multi-objective optimization control can improve the greenhouse climate control performance and reduce cost compared with other mentioned strategies.