GAN-based Model for Residential Load Generation Considering Typical Consumption Patterns

With the fast development of the Energy Internet, collecting and analyzing loads from the residential sector has become more and more important. However, the large-scale and real-time collection of residential loads still remains a big challenge due to high cost, technical barriers, and privacy concerns. Previous researches proposed two approaches for generating residential load profiles: bottom-up and top-down. However, these approaches suffer from either high complexity or low accuracy. In this work, we propose a residential load profiles generation model based on the Generative Adversarial Network (GAN). The GAN includes two independent networks: the generator and the discriminator which are trained against each other until achieving balance. Then we can produce synthetic profiles with the trained generator. To capture underlying features in load profiles, We transform them into matrices and implement convolution layers in networks. Furthermore, considering residents have different typical load patterns, We propose an advanced GAN based on the Auxiliary Classifier GAN (ACGAN) to generate profiles under typical modes. We use K-means clustering to acquire the pattern class of load profiles and train the model with the labeled data. Case studies on the dataset from an Irish smart meter trial show that the model can generate realistic load profiles under different load patterns without loss of diversity.