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

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.