This study proposes a method for home activity recognition solely from the cumulative power consumption data of individual circuits obtained from HEMS distribution boards, recorded every 30 minutes. The proposed method targets seven activities: waking up, going to bed, cooking, laundry, dishwashing, bathing, and personal hygiene, aiming to estimate which activity occurred in each 30-minute time slot. Initially, it identifies the circuits most closely related to each activity. For activities identifiable by the ON/OFF status of appliances, it uses the presence or absence of power consumption in the corresponding circuit to recognize them. For other activities, it constructs models to estimate their presence using machine learning based on specially designed features. Furthermore, it adapts to inter-household differences using transfer learning. We conducted experiments using one year’s HEMS data from 17 households through collaboration with a cooperative company. As a result, we confirmed that it could recognize each of the seven activities with an average F1 score of 0.86. Furthermore, we confirmed that the recognition accuracy of each activity could be improved by performing transfer learning.