A Hybrid Mode of Sequence Prediction Based on Generative Adversarial Network

Abstract

Human beings nowadays spend more than 90% of the lifetime indoors, leading to the dramatic increase of energy consumption in various buildings. Therefore, research regarding the environment friendly building becomes much more popular recently in which the prediction of energy consumption is a promised method. Nevertheless, the accuracy of prediction is not sound due to insufficient samples. A novel data generation model, termed HMSP, based on the generative adversarial networks, is proposed in this paper to generate much more data robustly, depending on a small number of samples available. The prediction CV-RMSE results, adopting data from the hybrid model, reach 3.03% at best and 7.99% at worst respectively compared to the samples recorded.