Online Generative Adversary Network Based Measurement Recovery in False Data Injection Attacks: A CyberPhysical Approach State estimation plays a critical role in maintaining operational stability of a power system, which is however vulnerable to attacks. False data injection (FDI) attacks can manipulate the state estimation results through tampering the measurement data. In this paper, a cyberphysical model is proposed to defend against FDI attacks. It judiciously integrates a physical model which captures ideal measurements, with a generative adversarial network (GAN) based data model which captures the deviations from ideal measurements. To improve computation efficiency of GAN, a new smooth training technique is developed, and an online adaptive window idea is explored to maintain the state estimation integrity in real time. The simulation results on IEEE 30bus system and IEEE 118bus system demonstrate that our defense technique can accurately recover the state estimation data manipulated by FDI attacks. The resulting recovered measurements are sufficiently close to the true measurements, with the error lower than   1.5e5   and   2e2   p.u. in terms of voltage amplitude and phase angle, respectively.

Generative Adversarial Learning Based Commercial Building Electricity Time Series Prediction Prediction on electricity load time series of commercial building and construction sets plays an important role in smart grid applications, building operation and environmental protection, etc. It differs significantly from the traditional aggregated electricity time series prediction problem and aims to provide more accurate prediction for each building respectively. In recent years, deep learning shows significant improvements on time series prediction over classical methods. However, it suffers from the problem of limited data. The problem is more serious in the context of electricity time series prediction of buildings. For instance, new buildings or buildings with newly installed meters have limited historical data, which results in poor model training and forecasts. In this paper we propose a novel generative adversarial learning based prediction method for dealing with this problem. We employ the generative adversarial network framework to obtain adversarially generated data for prediction model training by using long shortterm memory units based recurrent neural network and attention mechanism. Extensive experiments on reallife dataset demonstrate the improvements of our proposed method against the traditional methods.