توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Application of Neuro–Fuzzy models In Short Term Electricity Load Forecast
نویسنده(ها): ، ، ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۶
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 ohonen Classification and Intervention Analysis. The models are trained and assessed on load data extracted from EUNITE network competition.<\div>

۲Long Term Electrical Load Forecasting via a Neurofuzzy Model
نویسنده(ها): ، ، ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۶
Long–term forecasting of load demand is necessary for the correct operation of electric utilities. There is an on–going attention toward putting new approaches to the task. Recently, Neurofuzzy modeling has played a successful role in various applications over nonlinear time series prediction. This paper presents a neurofuzzy model for long–term load forecasting. This model is identified through Locally Linear Model Tree (LoLiMoT) learning algorithm. The model is compared to a multilayer perceptron and hierarchical hybrid neural model (HHNM). The models are trained and assessed on load data extracted from a North– American electric utility.<\div>
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