<![CDATA[Accurate short-term electricity load forecasting is essential for ensuring reliable energymanagement and maintaining power system stability, particularly in the household sector whereelectricity consumption exhibits highly dynamic and nonlinear patterns. Conventional forecastingmethods often have limited capability in capturing these complex temporal characteristics.Therefore, this study proposes a hybrid Convolutional Neural Network–Bidirectional Long ShortTerm Memory (CNN-BiLSTM) model to forecast 24-hour ahead household electricity demand basedon peak load data collected from Mojowarno District, Jombang Regency, Indonesia. The datasetconsists of hourly electricity consumption records from January 2024 to January 2025 and waspreprocessed through smoothing, outlier handling, and normalization before model training. Theproposed model combines CNN for automatic spatial feature extraction and BiLSTM for learningbidirectional temporal dependencies. Experimental results demonstrate excellent forecastingperformance with a Test Loss of 0.0024, Test MAE of 0.0562, Test RMSE of 0.0699, MAE of 3.4146kW, RMSE of 4.2470 kW, and an R² value of 0.9889. These findings indicate that the proposed CNNBiLSTM model effectively captures household electricity consumption patterns and providesaccurate short-term peak load forecasting, making it a promising approach for supporting energymanagement and electricity distribution planning]]>
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