Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand

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.