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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.

Recent changes in power system, e.g. large-scale penetration of renewable energy source, have led to change the behavior of the electricity demand and market price drastically; this behavior is also influenced by various factors such as weather and economic conditions. The factors that affect such demand and price are complex and the analysis of these factors enables stakeholders to construct an electricity business strategy. Therefore, a framework is needed to analyze and interpret the relationships among these demand, price, and factors. Construction of a graphical model is an attractive approach to describe the statistical relationships among these factors; the directed graphical model flexibly visualizes the direct/indirect factors affecting the demand and price. In this study, the authors applied a graphical modeling scheme using the sparse partially linear additive models for factor analysis of electricity demand and price. The scheme is applied to a real-world dataset and discussed from the viewpoint of interpretability.

In recent years, electricity power system structures have changed to accommodate large-scale penetration of renewable energy sources and energy-saving trends. Because of these system changes, the behavior of electricity net-loads, i.e. the difference between total demand and aggregate supply of distributed variable renewable energy sources, has changed drastically. This behavior is also influenced by various other factors such as weather, economic conditions, and consumer lifestyle. Analyzing the factors that affect the dynamic characteristics of the electricity net-load enables stakeholders to construct an electricity business strategy. Conventionally, such demand analysis has been conducted by targeting a limited number of explanatory variables that have been screened according to the prior knowledge of experts. The identification of essential explanatory variables through data-centric analysis, with a focus on variables that co-occur with demand, has long been recognized as important; however, discussion has been limited because it is difficult to describe plausible statistical relationships among the many possible explanatory variables with only a limited number of historical data samples available. This study focuses on the dynamics in hourly electricity demand and approach to identify annually important variables by constructing situation-dependent models. These models are based on the dataset consisting of demand and multiple explanatory variables that co-occur in the target time slices. The powerful concept of sparse modeling is applied to handle the large number of possible explanatory variables used in the situation-dependent modeling process. In particular, this study discusses inconsistency of the selected variables when the statistical models are constructed focusing on different data subsets; when the model is trained based on a dataset focusing on a specific time period, the selected variables may be significantly different from those resulting from a dataset focusing on another time period. The authors propose to derive a limited number of annually dominant variables by enumerating suboptimal models for each situation, and by selecting, as much as possible, essential variables that are commonly and consistently used for all situations. The proposed scheme was applied to a real-world demand dataset and discussed in the context of representation errors and interpretability. The results show that the proposed method is an effective approach for representing the situation-dependent impact of variables on demand.