Hybrid neurosymbolic causal inference Q-network (NS-CIQN) for optimizing energy efficiency in consumer electronics

The rapid growth of consumer electronics, such as smartphones, laptops, smart televisions, and home appliances, now accounts for a significant share of global electricity use and carbon emissions. Although most artificial intelligence (AI) energy-saving methods perform well, their limited interpretability often reduces trust among users and manufacturers. This study presents Neurosymbolic Causal Inference Q-Network (NS-CIQN), a hybrid Neurosymbolic framework that combines effectiveness with explainability. The framework uses causal graph neural networks to identify true cause-and-effect relationships, such as occupancy, time of day, temperature, and charging behavior. It also integrates symbolic reasoning rules that are both accessible and verifiable, along with a Deep Q-Network that learns optimal power-management strategies through reinforcement learning. A modified Whale Optimization algorithm fine-tunes solutions while preserving user comfort. Testing on the UK-DALE (UK Domestic Appliance-Level Electricity Dataset) and Reference Energy Disaggregation Dataset (REDD) household datasets, updated with 2025 regional carbon-intensity data, shows that NS-CIQN achieves an 88.4 % F1-score in recognizing appliance usage patterns, outperforming standard deep reinforcement learning and Random Forest methods, which score 81–83 %. NS-CIQN also reduces total energy consumption by 14.8–17.3 % in realistic simulations without affecting user comfort. The system’s causal graphs offer transparent, actionable explanations to support device manufacturers, smart-home platforms, and policymakers in advancing sustainability