Accurate short-term load forecasting is important for running power systems efficiently and managing smart grids. In this study, we present an improved Temporal Convolutional Network (TCN) model and compare eight optimization algorithms: Adam, AdaBelief, RAdam, Ranger, AdamP, NovoGrad, Adan, and SAM. We used hourly electricity load data from Denmark to test each optimizer under the same settings and with three different random seeds to ensure a fair and reliable comparison. All optimizers showed strong predictive accuracy, with root mean square error (RMSE) values between 0.03 and 0.05. Adan and AdamP had the lowest errors and were the most stable. These results show that the choice of optimizer has a big impact on how well TCN models learn and generalize. Our framework offers a solid benchmark for building adaptive and reliable forecasting systems for future smart grids.
Hmeda Musbah, Abdussalam Mohamed, Hamed H. Aly· 2026 IEEE Canadian Atlantic...· 0 citations
This paper proposes an electromagnetic (EM) environment classification framework that enables unmanned aerial vehicles (UAVs) and robotic platforms to perceive EM conditions as structured environmental states rather than unmodeled noise or isolated sensor faults. A physics-inspired, imbalanced multi-class dataset with nine EM-related features and six representative EM environment classes is generated to emulate realistic operating conditions spanning clean, disturbed, and hostile regimes. Heterogeneous classifiers—including Random Forest, Gradient Boosting, support vector machine (SVM) with radial basis function kernel, and a cost-sensitive feature-wise long short-term memory (LSTM) network—are trained using standardized features, stratified splits, and class-weighted learning to address minority yet safety-critical EM states. A stacked ensemble that fuses the probabilistic outputs of all base learners through a logistic regression meta-classifier achieves the best overall performance, with macro-averaged precision, recall, and F1-score of 0.9642, 0.9062, and 0.9319, respectively, outperforming individual models under statistically validated comparisons. The results demonstrate that multi-sensor EM awareness, implemented via ensemble learning, can provide robust and balanced classification of degraded and hostile EM environments, establishing EM perception as a key capability for resilient autonomous operation; future work will transition from synthetic to real robotic and flight data and integrate EM awareness into higher-level planning and control.
Abdussalam Mohamed, Hmeda Musbah, Hamed H. Aly· 2026 IEEE Canadian Atlantic...· 0 citations
A comfort-aware reinforcement learning (RL) framework is presented for intelligent indoor environment control in smart buildings. The system comprises three primary components: a data-driven comfort classifier, a lightweight indoor environment simulator, and a Deep Q-Network (DQN) control agent. A synthetic dataset, representing typical thermal and air-quality conditions, is pre-processed and used to train a Random Forest model that classifies occupant comfort in real time and translates these predictions into reward signals for the RL agent. The agent undergoes initial offline pre-training using a replay buffer populated with synthetic state–action–reward transitions, followed by further refinement through online interaction with the simulator to enhance sample efficiency and stability. Control performance is evaluated against a simple rule-based baseline over 100 episodes with varying internal gains and weather scenarios. The comfort classifier achieves high accuracy across all comfort categories, supporting reliable and consistent reward generation. As a result, the RL controller attains significantly higher comfort levels than the baseline while maintaining comparable or lower energy consumption. Pareto analysis indicates that the RL strategy consistently produces superior comfort–energy trade-offs, with most Pareto-optimal operating points attributed to the DQN agent. These results highlight the potential of RL as an adaptive, data-driven approach for multi-objective indoor environmental control in intelligent buildings.
Abdussalam Mohamed, Hmeda Najemeddin Musbah, Hamed H. Aly· 2026 IEEE Canadian Atlantic...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
