Robust Electricity Forecasting in Smart Buildings with Missing Data: A Concept Echo State Network Approach

Accurate electricity forecasting is essential for smart building energy optimization, yet dynamic usage and missing data pose significant challenges. This paper introduces a Concept Echo State Network (CESN) approach for robust forecasting. CESNs extract semantic concepts, constructing a dynamic matrix via a recursive process. A context-aware multi-objective optimization minimizes errors and maximizes robustness against missing data. A hierarchical fusion enhances adaptability. Evaluations on real-world datasets, simulating data gaps, demonstrate that CESNs outperform existing methods. This approach delivers superior accuracy and actionable insights, even with incomplete data. This research advances smart building energy management through interpretable, robust electricity prediction, directly addressing missing data challenges.