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

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% CO