Application of Pattern Recognition and Artificial Neural Network to Load Forecasting in Electric Power System

• Published in: Third International Conference on Natural Computation (ICNC 2007) Vol. 1; pp. 381 - 385
• Main Authors: Wenjin Dai, Ping Wang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2007
• Subjects: artificial neural network (ANN); Artificial neural networks; back propagation (BP); Control systems; Economic forecasting; Energy management; learning algorithm; load forecast; Load forecasting; Medical services; Numerical models; Pattern recognition; Power system modeling; Predictive models
• ISBN: 9780769528755; 0769528759
• ISSN: 2157-9555
• DOI: 10.1109/ICNC.2007.260

Electric power system load forecasting plays an important role in the energy management system (EMS), which has great influence on the operation, controlling and planning of electric power system. A precise electric power system short term load forecasting will result in economic cost saving and improving security operation condition. With the development of deregulation in electric power system, the method of short term load forecasting with high accuracy is becoming more and more important. Due to the complicacy and uncertainty of load forecasting, electric power load is difficult to be forecasted precisely if no analysis model and numerical value algorithm model is applied. In order to improve the precision of electric power system short term load forecasting, a new load forecasting model is put foreword in this paper .This paper presents a short-term load forecasting method using pattern recognition which obtains input sets belong to multi-layered fed-forward neural network, and artificial neural network in which BP learning algorithm is used to train samples. Load forecasting has become one of the major areas of research in electrical engineering in recent years. The artificial neural network used in short-time load forecasting can grasp interior rule in factors and complete complex mathematic mapping. Therefore, it is world wide applied effectively for power system short-term load forecasting.