Renewable energy sources are now being used with buildings like PV panels. Consequently, short-term household load forecast plays an important role in managing distributed energy generation, local consumption, and grid-building integration. Forecasting household load, however, can be an intractable problem. These loads are characterized by large uncertainty and variations, leaving much room to improve accuracy. To improve the household load forecast accuracy, this paper advocates a Kalman filter-based bottom-up approach. First, using a deep learning model and a persistence model on public datasets, the authors verified the advantage of the bottom-up approach through granularity analysis at the appliance, room, house levels. Employing the Symmetric Mean Absolute Percentage Error, the authors compared two strategies: (1) the conventional strategy, which forecasts the load directly at the household level, and (2) the bottom-up strategy, which aggregates the forecasts made at the room or appliance level. Experimental results on public datasets demonstrated that the bottom-up approach holds great promise. Second, as the bottom-up approach is often criticized for the cost, the authors designed a recontextualized Kalman filter model to efficiently forecast appliance energy usages. Using two strategies, the authors compared the Kalman filter-based bottom-up approach with deep-learning models. They found the bottom-up approach reduced forecast errors 49% more than the deep-learning models and 47% more than the conventional strategy. Finally, the authors concluded that a Kalman filter-based bottom-up approach could efficiently improve household load forecast accuracy. The findings could help give fast and accurate load forecasts for building energy management and predictive controls.