Title :
Online daily load forecasting based on support vector machines
Author :
Xu, Hong ; Wang, Jian-Hua ; Zheng, Shi-Quan
Author_Institution :
Inst. of Electr. Eng., Xi´´an JiaoTong Univ., China
Abstract :
In this paper a new online daily load forecasting (ODLF) approach is proposed for short term electrical load forecasting in practice. It incorporates the load forecasting algorithm based on the support vector machines (SVMs) with OPC specifications and becomes a standard online load forecasting component. By carefully selecting the load features in the electric power system and using the improved sequential minimal optimization algorithm, this algorithm can accurately predict the next day´s load trend online. Especially the SVMs-based ODLF approach showed satisfied performance, such as powerful regression ability, acceptable predict accurateness and perfect foundation in theory. As an independent functional modular, the designed ODLF component can be used in any OPC-compatible environment for real-time load forecasting in distributed electrical system.
Keywords :
learning (artificial intelligence); load forecasting; power engineering computing; real-time systems; regression analysis; support vector machines; OPC specification; distributed electrical system; electric power system; electrical load forecasting; load feature selection; load trend prediction; online daily load forecasting; real-time load forecasting; sequential minimal optimization algorithm; support vector machines; Economic forecasting; Load forecasting; Machine learning; Power system modeling; Power system planning; Power system simulation; Predictive models; Quadratic programming; Real time systems; Support vector machines; OPC; Online Daily Load Forecasting; SVMs;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527634
