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Development of peak load forecasting system using neural networks and fuzzy theory
Author(s) -
Ueki Yoshiteru,
Matsui Tetsuro,
Endo Hiroshi,
Kato Tatsuyosi,
Araya Ryosaku
Publication year - 1996
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.4391170304
Subject(s) - artificial neural network , fuzzy logic , workstation , reliability (semiconductor) , computer science , electric power system , mean absolute percentage error , expert system , scheduling (production processes) , artificial intelligence , reliability engineering , machine learning , simulation , data mining , power (physics) , engineering , operating system , operations management , physics , quantum mechanics
Abstract This paper presents a peak load forecasting system using multilayer neural networks and fuzzy theory. Electric load forecasting in power systems is a very important task from the perspective of reliability and economic operation. Daily peak load forecasting is one of the basic operations of generation scheduling for the following day. Therefore, many statistical methods have been developed and used for such forecasting even though it has been difficult to construct a proper functional model. The developed system is applied by neural network and fuzzy theory to forecast for daily, weekly and monthly peak load. The system consists of an engineering workstation (EWS) and a personal computer (PC). The EWS is for learning and data‐bases, and the PC is for man‐machine interface such as forecasting operation. The system has been used since June 1993. The result evaluated with an absolute mean error is 1.63 percent for 10 months. From the results shown here, the system applied by neural network and fuzzy theory has high validity.