An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders

• Published in: Journal of parallel and distributed computing Vol. 117; pp. 267 - 273
• Main Authors: Tong, Chao, Li, Jun, Lang, Chao, Kong, Fanxin, Niu, Jianwei, Rodrigues, Joel J.P.C.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2018
• Subjects: Deep learning; Feature extraction; Multi-modal; Stacked denoising auto-encoders; Support vector regression; Deep learning; Feature extraction; Support vector regression; Multi-modal; Stacked denoising auto-encoders
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2017.06.007

In real word it is quite meaningful to forecast the day-ahead electricity load for an area, which is beneficial to reduction of electricity waste and rational arrangement of electric generator units. The deployment of various sensors strongly pushes this forecasting research into a “big data” era for a huge amount of information has been accumulated. Meanwhile the prosperous development of deep learning (DL) theory provides powerful tools to handle massive data and often outperforms conventional machine learning methods in many traditional fields. Inspired by these, we propose a deep learning based model which firstly refines features by stacked denoising auto-encoders (SDAs) from history electricity load data and related temperature parameters, subsequently trains a support vector regression (SVR) model to forecast the day-ahead total electricity load. The most significant contribution of this heterogeneous deep model is that the abstract features extracted by SADs from original electricity load data are proven to describe and forecast the load tendency more accurately with lower errors. We evaluate this proposed model by comparing with plain SVR and artificial neural networks (ANNs) models, and the experimental results validate its performance improvements. •We propose a deep learning based model for forecasting day-ahead electricity load.•It uses history load data, weather and season parameters.•It uses multiple stacked denoising auto-encoders to extract features.•The refined features and a season parameter are fed into a SVR model for training.