<![CDATA[?Real-time monitoring of household electricity consumption is not yet sufficient to support adaptive energy management. Therefore, an accurate yet easily interpretable prediction capability is required. This study implements the Prophet algorithm, an additive time series model based on trend and seasonal components, as the core method for predicting daily energy consumption in an Internet of Things (IoT)-based system with per-room granularity. Data were obtained from PZEM-004T sensors and NodeMCU ESP32 modules in three rooms over 30 days, processed through a two-stage grid search procedure for model hyperparameter optimization. The evaluation results show a testing MAPE of 1.05%–2.01% across the three rooms, all falling into the highly accurate category (<10%). Furthermore, the average MAPE difference between the training and testing data reached only 0.42 percentage points, indicating good model generalization without overfitting. Component decomposition analysis reveals that the consumption pattern is dominated by a stable linear trend with a low-amplitude weekly seasonal pattern (±0.06 kWh), thereby providing a higher level of interpretability compared to black-box models. The 30-day-ahead projection yields a total estimated consumption of approximately 384 kWh (~IDR 554,817) for the three rooms, which can be utilized as a basis for budget planning and adaptive electrical load management. The main contribution of this study is a transparent and reproducible Prophet tuning procedure for per-room electricity consumption data with limited historical volume, supported by metrological validation of the acquisition sensor as an input quality assurance step, a context that has not been widely explored in prior Prophet literature]]>
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