Missing Load Situation Reconstruction Based on Generative Adversarial Networks

The completion and the correction of measurement data are the foundation of the ubiquitous power internet of things construction. However, data missing may occur during the data transporting process. Therefore, a model of missing load situation reconstruction based on the generative adversarial networks is proposed in this paper to overcome the disadvantage of depending on data of other relevant factors in conventional methods. Through the unsupervised training, the proposed model can automatically learn the complex features of loads that are difficult to model explicitly to fill the incomplete load data without using other relevant data. Meanwhile, a method of online correction is put forward to improve the robustness of the reconstruction model in different scenarios. The proposed method is fully data-driven and contains no explicit modeling process. The test results indicate that the proposed algorithm is well-matched for the various scenarios, including the discontinuous missing load reconstruction and the continuous missing load reconstruction even massive data missing. Specifically, the reconstruction error rate of the proposed algorithm is within 4% under the absence of 50% load data.