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Amid the ongoing acceleration of urban development, tertiary sector consumption and household electricity demand have emerged as the dominant components of overall municipal power consumption. Such loads are highly sensitive to unpredictable factors such as occupant behavior and meteorological conditions, which often lead to irregular deviations from expected trends and, consequently, considerable prediction inaccuracies. The present study introduces an integrated prediction approach to improve forecast precision for city-wide electricity demand, particularly across intervals characterized by anomalous consumption shifts. Initially, a systematic metric is developed to detect and quantify these irregular load excursions by analyzing the residual series obtained through decomposition of the raw load sequence. Next, a data-expansion strategy grounded in Generative Adversarial Networks is developed to synthetically augment the otherwise sparse set of anomalous instances, thereby enriching the dataset available for model training. Lastly, the sophisticated TimesNet architecture is adopted to effectively model the intricate, nonlinear dynamics observed in load behavior throughout periods of unusual variation. Validation experiments performed on real-world consumption records from Chongqing, China, substantiate the performance gains achieved by the suggested framework.