Probabilistic Forecasting of Household Energy Self-Sufficiency Rate Using Pre-Trained Time-Series Foundation Models with Monte Carlo Simulation

Evaluating energy self-sufficiency in the residential sector is crucial for decarbonization. However, the discrepancy between design-stage estimates and actual measurements (the performance gap) poses a significant challenge. While the primary cause of this gap lies in uncertainties stemming from occupant behavior and weather conditions, no medium-term probabilistic forecasting framework for the energy self-sufficiency rate (ESSR) incorporating these factors has been established. To address this issue, this study proposes a probabilistic forecasting framework that integrates a pre-trained time-series foundation model called Chronos with Monte Carlo simulation. Validation using real data from 39 households demonstrates that the proposed method can achieve prediction accuracy superior to baseline models. Furthermore, the derived probability distributions of ESSR quantify fluctuation risks across households and seasons, highlighting the limitations of conventional uniform evaluation models.