Publication Type : Conference Paper
Publisher : Cybernetics and Mathematics Applications in Intelligent Systems, Springer International Publishing
Source : Cybernetics and Mathematics Applications in Intelligent Systems, Springer International Publishing, Volume 574, Cham, p.254-263 (2017)
Url : https://link.springer.com/chapter/10.1007/978-3-319-57264-2_26
ISBN : 9783319572642
Keywords : Artificial Neural Network (ANNs), Ensemble Regression Trees (ERT), Extreme Learning machines (ELM), Hodrick-Prescott (HP), Machine learning
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2017
Abstract : In a Smart grid, implementation of value-added services such as distribution automation (DA) and Demand Response (DR) [1] rely heavily on the availability of accurate electricity consumption forecasts. Machine learning based forecasting systems, due to their ability to handle nonlinear patterns, appear promising for the purpose. An empirical evaluation of eight machine learning based systems for electricity consumption forecasting, based on Extreme Learning machines (ELM), Ensemble Regression Trees (ERT), Artificial Neural Network (ANNs) and regression is presented in this study. Forecasting systems thus designed, are validated on consumption data collected from 5275 users. Result indicate that ELM based electricity consumption forecasting systems are not only more accurate than other systems considered, they are considerably faster as well.
Cite this Research Publication : J. A. Balaji, Ram, D. S. Harish, and Dr. Binoy B. Nair, “Machine Learning Approaches to Electricity Consumption Forecasting in Automated Metering Infrastructure (AMI) Systems: An Empirical Study”, in Cybernetics and Mathematics Applications in Intelligent Systems, Cham, 2017, vol. 574, pp. 254-263.
