Nowadays, residential households, including both consumers and emerging prosumers, have exhibited a growing demand for active/reactive power. This demand surge arises from activities such as charging electrical devices, leveraging flexible resources, and integrating renewable energy sources. To meet this escalating demand effectively, operators must ensure the provision of an ample supply of active/reactive power. Achieving this necessitates the identification of influential factors and the generation of precise forecasts for active/reactive power demand. Hence, this work proposes an efficient hybrid deep learning model consisting of long short-term memory and self-Attention (LSTM-Attention). This model incorporates explicit time encoding to forecast one-hour-ahead consumption of active and reactive power using real-time data from residential units. The integration of both models represents a strategic development for model robustness. Leveraging the inherent strengths of both architectures allows for a synergistic compensation that addresses limitations within each, contributing to an overall effective forecasting model. Moreover, the Shapley Additive Explanations (SHAP) framework was employed for model interpretability, and the investigation underscores the pivotal role of incorporating temporal features into active and reactive power forecasting. SHAP findings can be effectively applied in power management strategies to optimally enhance demand response. Finally, to evaluate the effectiveness of the proposed model, a comprehensive array of performance metrics was employed. The results demonstrate a superior forecast accuracy of the proposed model compared to alternative forecasting models. The proposed model achieved the lowest root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for active power with a value of 0.0256, 0.0181, and 14.255 %, respectively. The formulated forecasting method can also significantly contribute to the industrial sector by improving the accuracy of active/reactive power forecasting, thereby enhancing model interpretability and identifying the most critical factors.