Vulnerability Assessment of Machine Learning Based Short-Term Residential Load Forecast against Cyber Attacks on Smart Meters

Short-term load forecast at residential house level plays a critical role in home energy management system. While a variety of machine learning based load forecasting methods have been proposed, their prediction performance have not been assessed against cyber threats on smart meters which have been increasingly reported. This thesis investigates the vulnerability of four extensively used machine learning algorithms for residential short-term load forecast against cyberattacks, including nonlinear auto regression with external input (NARX) neural network, support vector machine (SVM), decision tree (DT), and long-short-term memory (LSTM) deep learning. The predication performances on these two datasets are compared by using NARX, SVM, DT, and LSTM. Four cyberattack models are investigated, including pulse attack, scale attack, ramp attack, and random attack.