Stackade Ensemble Learning for Resilient Forecasting Against Missing Values, Adversarial Attacks, and Concept Drift

Machine learning is often implemented in smart grids to help with electricity load forecasts, which are challenging to process or compute using conventional approaches. However, most of the proposed techniques were created with the assumption that the data was clean with a consistent distribution. However, this is not always the case because there can be missing data due to distributed denial-of-service attacks, changes in data patterns that make predictions less accurate, and small perturbations from adversarial attacks that trick the machine learning into making wrong forecasts. While several approaches have been suggested, they are limited to a trivial solution, and the sequential operation needed to correct the input will accumulate errors from inadequate correction. This research proposes a novel approach called Stackade Ensemble Learning. It works by cascading the dependent input corrections with enhanced forecasting models to reduce the error accumulation. Then, the outputs were stacked to combine the results and improve the forecast. The results show 348.4002% of mean absolute error score improvement against the federated solution and 30.3783% against the trivial solution on compounded problem, proving its effectiveness.