<p>Accurate forecasting of electricity consumption in residential buildings is essential for effective energy planning, real-time load balancing, and demand-side management. As residential demand becomes increasingly variable due to the proliferation of smart appliances and renewable energy systems, robust and adaptive forecasting models are crucial. To address this need, this study introduces a hybrid forecasting model that combines a deep Temporal Convolutional Network (TCN) with a Fuzzy Wavelet Neural Network (FWNN). The TCN component captures long-range temporal dependencies in time-series data, while the FWNN integrates fuzzy logic and wavelet transforms within neural network architecture, allowing the model to effectively handle uncertainty, imprecision, and nonlinearity commonly observed in electricity consumption behaviors. To further improve predictive accuracy, the model’s hyperparameters are fine-tuned using the Aquila Optimization metaheuristic algorithm. The proposed model is evaluated on two real-world datasets containing minute-level and hourly electricity consumption records. Comparative experiments with several established baseline models show that the suggested hybrid approach consistently outperforms its rivals across key performance metrics. These results underscore the model’s accuracy and robustness, positioning it as a promising candidate for integration into modern residential energy management systems.</p>