POWER CONSUMPTION FORECASTING BY HYBRID DEEP ARCHITECTURES WITH DATA FUSION

Many of the deep learning solutions for time-series forecasting reported in the literature include complex neural networks that may not be directly employed by the practitioner in the field. In this study, we demonstrate how the standard deep neural network types, convolutional neural network (CNN) and long short-term memory (LSTM) network can be applied in the field of time-series forecasting. This study consists of two parts. The first part is to compare CNN and LSTM models with classical methods like Random Forest (RF) and ARIMA for the univariate electric power consumption task. The second part is to use the best performing model from the first part in the hybrid model and perform data fusion with the newly built hybrid model for the electric power consumption forecasting task. CNN and LSTM models outperform traditional methods when their performances are evaluated on the univariate electric power consumption data of Illinois, USA. We also illustrate the use of hybrid deep learning models composed of standard CNN and LSTM for data fusion with the aim of time-series forecasting. When the hybrid models are applied to the fused data of the electric power consumption data and the multivariate weather data of Illinois, USA, the forecasting performance is improved compared to that when only univariate data is used.