Robust regression for electricity demand forecasting against cyberattacks

Standard methods for forecasting electricity loads are not robust to cyberattacks on demand data, potentially leading severe consequences such as major economic loss or a system blackout. Methods required that can handle under these conditions and detect outliers would otherwise go unnoticed. The key challenge is remove many possible while maintaining enough clean data use in the regression. In this paper we investigate approaches with data-driven tuning parameters, particular present an adaptive trimmed regression method better provide improved forecasts. general, perform much than their fixed parameter counterparts. Recommendations future work provided.