COGEniX: an industrial informatics framework for cognitive energy forecasting and carbon-aware scheduling in smart...

Industrial energy management faces escalating challenges from rising electricity costs, volatile carbon intensity and the operational complexity of integrating renewables and storage. Traditional approaches, ranging from heuristic rules to black-box machine learning models, struggle to balance adaptability, transparency and deployability. Heuristic rules fail to adjust to dynamic operating conditions, while opaque models undermine operator trust, and deployment friction often prevents reliable execution on edge hardware. As a result, factories continue to suffer from inefficiency, idle consumption and missed opportunities for cost-effective decarbonization. This study introduces COGEniX (Cognitive Energy Informatics and eXecution), a unified framework that integrates calibrated multi-horizon forecasting, reinforcement learning (RL) based scheduling, explainability and deterministic edge deployment. Experiments on an adapted CityLearn dataset simulating microgrid-style industrial zones compare COGEniX against three representative baselines such as a rule-based scheduler using time-of-use heuristics, a gradient-boosted regression forecaster with deterministic dispatch, and a short-horizon model predictive controller. Validation on the Kaggle multi-floor smart building dataset demonstrates 44.0% lower MAE and 39.3% lower RMSE with 15.3% energy savings and 18.1% CO₂ reduction at edge-feasible latency, confirming both predictive and operational gains. Patch-based transformers generate calibrated load and price forecasts that expose temporal drivers, while a Soft Actor-Critic scheduler optimizes a joint objective over cost and carbon under uncertainty and process constraints. SHAP (SHapley Additive exPlanations) explanations align with attention to yield interpretable decision narratives, and ONNX(Open Neural Network Exchange)/TorchScript export with pruning, quantization and profiling ensures compliance with latency and memory limits on real devices. Evaluation across CityLearn industrial zones shows up to a 27% reduction in operational cost and a 31% decrease in CO₂ emissions compared with the strongest baseline, maintaining robustness under forecast noise. By coupling forecasting, control, interpretability and deployment in a cohesive pipeline, COGEniX advances industrial energy management toward adaptive, carbon-aware and auditable decision-making that aligns profitability with sustainability.