Sequential pattern mining — A study to understand daily activity patterns for load forecasting enhancement

Abstract

Load forecasting at appliance-level or house-level is a key to develop efficient Demand Side Management programs. Lots of recent research work have pointed out that load curves at household's level depend highly on human behaviors and activities. However, the state-of-the-art load modeling approach takes only individual human activities with appliance-level time-of-use data into account. There are few studies about influence of sequences of activities performed throughout a day on power consumption at household's level. In this work, we conduct a broad study of activity sequences in daily life that influence power consumption of individual households. A context-rich data set including daily activity information and power consumption measurements from 23 households is collected across Japan. The contribution of this paper is twofold: 1) a set of insights into household-specific activity sequences influencing power consumption derived from a sequence mining algorithm, in order to identify significant associations between power consumption and household-specific activity sequences; 2) a load forecasting study using identified frequent activity sequences as an enhancement. Our analysis on sequence-based rules shows potential for inferring future activities and the power consumption of the corresponding activity. Finally, we demonstrate how very short-term load forecasting, like 15 minutes ahead, can benefit from activity sequences for individual households.