مشاهده مشخصات مقاله
Application of Neuro-Fuzzy models In Short Term Electricity Load Forecast
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نویسنده (ها) |
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A. R. Koushki
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M. Nosrati Maralloo
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C. Lucas
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A. Kalhor
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مربوط به کنفرانس |
چهاردهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران |
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چکیده |
One of the important requirements for operational
planning of electrical utilities is the prediction of
hourly load up to several days, known as Short Term
Load Forecasting (STLF). Considering the effect of its
accuracy on system security and also economical
aspects, there is an on-going attention toward putting
new approaches to the task. Recently, Neuro Fuzzy
modeling has played a successful role in various
applications over nonlinear time series prediction.
This paper presents a neuro-fuzzy model for the
application of short-term load forecasting. This model
is identified through Locally Liner Model Tree
(LoLiMoT) learning algorithm. The model is compared
to a multilayer perceptron and Kohonen Classification
and Intervention Analysis. The models are trained and
assessed on load data extracted from EUNITE network
competition. |
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قیمت |
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برای اعضای سایت : ۱٠٠,٠٠٠ ریال
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برای دانشجویان عضو انجمن : ۲٠,٠٠٠ ریال
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برای اعضای عادی انجمن : ۴٠,٠٠٠ ریال
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