In recent years, plant factories have been drawing attention for their capability to solve global food crisis. However, the energy cost of plant factories is high due to the power consumption of air conditioning and cultivation systems necessary for realizing semi-automatic production. As this high cost hinders their propagation,, plant factories must minimize the energy cost of growing plants. We propose an operation planning method of cultivation systems for minimizing energy cost minimization while producing plants with the same amount and quality. The effect of electricity charge and weather factors on electric power consumption under various real-world constraints are used to decide the appropriate operation plan of cultivation systems. Simulation results show that the operation plan of cultivation systems properly reflects the effect of electricity charge and weather factors on electric power consumption to reduce energy cost. ARTICLE HISTORY

In recent decades, there has been a gradual penetration of plant factories achieving semiautomated crop cultivation. However, efficient energy utilization, as well as quality control of crops, are very important factors with regard to sustainable operation. Operating parameters, such as room temperature, affect not only the quality of crops but also the electric power required to realize the target operation while being influenced by the environment outside the plant. Therefore, a methodology is needed to analyze and interpret the relationships among these manipulated variables, exogenous variables, crop quality, and the amount of required electric power. Constructing a directed acyclic graph composed of regression models is an attractive approach for such analysis; however, the relationships can possibly be nonlinear, so the direct application of existing analytic approaches will not be appropriate. In this paper, we propose a methodology for relationship analysis among variables based on the directed acyclic graphs while identifying the linearity/nonlinearity in their relationships. In general, the construction of such a graphical model has computational issues, especially when the number of variables is large, and the risk of overfitting. The proposed method utilizes the idea of sparse regularization, which has been actively discussed in the field of machine learning, for realizing the automatic identification of linearity/nonlinearity between variables and screening redundant candidate structures; this approach relaxes the computational complexity issue and controls the risk of overfitting. As a case study, the proposed method is applied to a dataset collected from a real-world cultivation system in a plant factory to discuss its usefulness. INDEX TERMS Analysis of plant data, directed graphical model, energy-aware plant growth control, identification of linearity/nonlinearity, overlap group lasso, plant factory, sparse partially linear model.