A Monitoring-Oriented Digital Twin Pipeline for Data-Driven Prediction of Residential Prosumer Energy Profiles

Residential prosumer buildings with PV and high-load subsystems exhibit irregular demand, yet current residential digital-twin and baselining studies rarely address missing-data realism and causal deployment constraints. This paper presents a monitoring-oriented operation-phase digital twin analytics pipeline for an instrumented villa in Segrate, Italy, linking whole-building demand, PV output, pool-circuit power, and outdoor air temperature to a one-hour-ahead baseline task. A fixed Random Forest is evaluated across eight dataset configurations against persistence and Multiple Linear Regression. Under deployment-realistic causal conditions, the best configuration achieves RMSE 576.5 W, demonstrating the value of a traceable leakage-safe baselining workflow for residential prosumer settings.