Comparison of Deep Learning Architectures for Short-Term Electrical Load Forecasting Based on Multi-Modal Data

Abstract

Short-term load forecasting is a topic of considerable interest as it is of major importance for specifying and managing power resources and needs. In the literature, Deep Neural Networks have been successfully recently applied in load forecasting using single modalities as an improvement to traditional Artificial Neural Networks (ANN). In this paper, the main objective is to tackle the load forecasting problem with the intention of enhancing the prediction performance by combining and testing different multi-modal deep learning approaches and architectures in order to process and relate information from multiple modalities. The benefits of using multi-modality instead of one modality, applied to the time series modeling problem of short-term load forecasting is therefore investigated. The models are trained and evaluated using the hourly temperature and electrical consumption in addition to auto-regressive variables as the first modality. The day type characteristic, such as: weekends, week days, bank holidays, religious holidays etc., may be considered as a second modality. The approach liability is tested by comparing the empirical results of different Deep architectures namely: Stacked Denoising Auto- Encoders (SDAEs) and Convolutional Neural Network (CNN), with multiple and single modalities where processing the same task of one day ahead load forecasting.