Short term load forecasting using a hybrid neural network

• Main Authors: Yap K.S., Abidin I.Z., Lim C.P., Shah M.S.
• Other Authors: 24448864400
• Format: Conference paper
• Published: 2023
• Subjects: 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

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...