This paper pursues the question of how seasons of the year affect disaggregation performance in Non-Intrusive Load Monitoring. To this end, we select the dishwasher, a common household appliance that may exhibit usage cycles depending on the user. We utilize an auto-correlation function to detect usage patterns of dishwashers in each season. Then, we examine the dissimilarity across each season with the help of the Keogh Lower Bound measure. Finally, we conduct a disaggregation study using the REFIT dataset and relate the outcome to the dissimilarity across seasons. Our findings indicate that in cases where energy consumption shows similarity throughout seasons, the performance of load disaggregation approaches can be positively affected.
Index Terms— NILM, Seasonality, Auto-Correlation, Similarity, Performance