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Deep neural network based demand side short term load forecasting

In smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analysis to recent machine learning approach and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management including individual load forecasting is becoming critical. In this paper, we propose deep neural network (DNN) based load forecasting models, and apply them to demand side empirical load database. DNNs are trained by two different ways: pre-training restricted Boltzmann machine and using rectified linear unit without pre-training. DNN forecasting models are trained by individual customer's electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with shallow neural network (SNN), and double seasonal Holt-Winters (DSHW) model. The mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) are used for verification. The results show that DNNs exhibit accurate and robust forecasts compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN, and 9% and 29% compared to DSHW.