Robust regression for electricity demand forecasting against cyberattacks

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