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Long‐Term Load Forecasting Using Grey System Theory
Author(s) -
Morita Hironobu,
Zhang DePing,
Tamura Yasuo
Publication year - 1995
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.4391150202
Subject(s) - term (time) , interval (graph theory) , demand forecasting , electric power system , computer science , probabilistic forecasting , point (geometry) , mathematical optimization , operations research , econometrics , power (physics) , mathematics , artificial intelligence , physics , geometry , combinatorics , quantum mechanics , probabilistic logic
Abstract Supply and demand in power system planning and operation is required to be balanced. An operational reserve for protection against faults or accidental demands also is required. Therefore load forecasting is one of the most important fields and various load forecasting methods have been applied. In this paper the grey system theory which mats uncertain information is applied to the long‐term load forecasting from three aspects: the point prediction; the interval prediction; and the topological forecasting. In the point prediction, the annual total demand is predicted, in the interval prediction, the annual peak demand is predicted, and in the topological forecasting, the date where a yearly maximum peak demand would occur is predicted. The grey dynamic model (abbreviated as GM model) is adopted as the predicted model. The GM model is a differential equation model which is different from most forecasting models. The GM model is quite powerful when combined with the preliminary transformation called the accumulated generating operation (AGO). This paper proposes a new method for the long‐term load‐forecasting problems involving uncertainty. The predicted results have been found to be very satisfactory. The grey system theory is a new tool which is very efficient for load forecasting.