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 short-term memory units based recurrent neural network and attention mechanism. Extensive experiments on real-life dataset demonstrate the improvements of our proposed method against the traditional methods.