论文标题
使用贝叶斯神经网络进行阳光预测的温室中的最佳照明控制
Optimal Lighting Control in Greenhouses Using Bayesian Neural Networks for Sunlight Prediction
论文作者
论文摘要
控制环境参数，包括温室中的光，会增加农作物的产量；但是，补充照明的电力成本可能很高。因此，出现应用具有成本效益的照明方法的重要性。在本文中，考虑到用于阳光预测的变异推理贝叶斯神经网络（BNN）模型，开发了一种最佳的补充照明控制方法。通过测试位于北卡罗来纳州的站点的历史太阳能数据（$ r^{2} $ = 0.9971，RMSE = 1.8％）来验证预测模型。提出的照明方法被证明是通过考虑基于BNN的阳光预测，植物光需求和可变的电力定价来最大程度地降低电力成本。为了进行评估，将新策略与：1）基于马尔可夫的预测方法进行了比较，该方法解决了相同的优化问题，假设Markov模型用于阳光预测； 2）旨在提供固定量的光的启发式方法。进行了仿真研究以检查基于BNN的方法的电力成本提高。结果表明，与基于马尔可夫预测的方法和一年中的启发式方法相比，基于BNN的方法平均降低了（平均）2.27％和43.91％。
Controlling the environmental parameters, including light in greenhouses, increases the crop yield; however, the electricity cost of supplemental lighting can be high. Therefore, the importance of applying cost-effective lighting methods arises. In this paper, an optimal supplemental lighting control approach is developed considering a variational inference Bayesian Neural Network (BNN) model for sunlight prediction. The predictive model is validated through testing the model on the historical solar data of a site located at North Carolina ($R^{2}$=0.9971, RMSE=1.8%). The proposed lighting approach is shown to minimize electricity cost by considering the BNN-based sunlight prediction, plant light needs, and variable electricity pricing when solving the underlying optimization problem. For evaluation, the new strategy is compared to: 1) a Markov-based prediction method, which solves the same optimization problem, assuming a Markov model for sunlight prediction; 2) a heuristic method which aims to supply a fixed amount of light. Simulation studies are conducted to examine the electricity cost improvements of the BNN-based approach. The results show that the BNN-based approach reduces cost by (on average) 2.27% and 43.91% compared to the Markov prediction-based method and the heuristic method, respectively, throughout a year.
