Deep learning framework to forecast electricity demand

• Published in: Applied energy 2019-03, Vol.238, p.1312-1326
• Main Authors: Bedi, Jatin, Toshniwal, Durga
• Format: Article
• Language: English
• Subjects: Active forecasting; Electricity demand prediction; Energy analytic; LSTM network; Recurrent neural network
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2019.01.113

[Display omitted] •A window-based multi-input-multi-output model is proposed for demand forecasting.•The model actively learns historical data dependencies without making any prior assumptions.•Active learning is introduced by coupling historical data with recent demand observations.•The proposed window-based network model outperforms other state-of-the-art prediction models. The increasing world population and availability of energy hungry smart devices are major reasons for alarmingly high electricity consumption in the current times. So far, various simulation tools, engineering and Artificial Intelligence based methods are being used to perform optimal electricity demand forecasting. While engineering methods use dynamic equations to forecast, the AI-based methods use historical data to predict future demand. However, modeling of nonlinear electricity demand patterns is still underdeveloped for robust solutions as the existing methods are useful only for handling short-term dependencies. Moreover, the existing methods are static in nature because they are purely historical data driven. In this paper, we propose a deep learning based framework to forecast electricity demand by taking care of long-term historical dependencies. Initially, the cluster analysis is performed on the electricity consumption data of all months to generate season based segmented data. Subsequently, load trend characterization is carried out to have a deeper insight of metadata falling into each of the clusters. Further, Long Short Term Memory network multi-input multi-output models are trained to forecast electricity demand based upon the season, day and interval data. In the present work, we have also incorporated the concept of moving window based active learning to improve prediction results. To demonstrate the applicability and effectiveness of the proposed approach, it is applied to the electricity consumption data of Union Territory Chandigarh, India. Performance of the proposed approach is evaluated by comparing the prediction results with Artificial Neural Network, Recurrent Neural Network and Support Vector Regression models.