所蔵: Short term load forecasting using a hybrid neural network

Short term load forecasting using a hybrid neural network

Short Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the...

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書誌詳細
主要な著者:	Yap K.S., Abidin I.Z., Lim C.P., Shah M.S.
その他の著者:	24448864400
フォーマット:	Conference paper
出版事項:	2023
主題:	Gaussian adaptive resonance theory Generalized regression neural network Load forecasting Time series prediction Artificial intelligence Competition E-learning Education Electric load forecasting Electric power systems Finance Food processing Forecasting Gaussian distribution Genetic algorithms Image classification Internet Loads (forces) Regression analysis Statistics Trellis codes Vegetation (PL) properties Adaptive resonance theory (ART1) Computational costs Financial performances gaussian Gaussian regression Generalized regression neural network (GRMM) Grid operations Hybrid neural network (HNN) International (CO) learning processes load demand On line learning power systems prediction accuracy real values Regression (R2) Regression problems Reliability and stability short term Short term load forecasting Short term load forecasting (STLF) simulation results Support vector regression (SVR) Time frames training samples Unsupervised clustering Utility companies Neural networks
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Short term load forecasting using a hybrid neural network

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