Advanced Methods of Power Load Forecasting

• Muut tekijät: García-Díaz, J. Carlos (Toimittaja), Trull, Óscar (Toimittaja)
• Aineistotyyppi: Elektroninen Kirjan osa
• Kieli: englanti
• Julkaistu: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Aiheet: Research & information: general; Physics; Prophet model; Holt-Winters model; long-term forecasting; peak load; prophet model; multiple seasonality; time series; demand; load; forecast; DIMS; irregular; galvanizing; short-term electrical load forecasting; machine learning; deep learning; statistical analysis; parameters tuning; CNN; LSTM; short-term load forecast; Artificial Neural Network; deep neural network; recurrent neural network; attention; encoder decoder; online training; bidirectional long short-term memory; multi-layer stacked; neural network; short-term load forecasting; power system
• Linkit: DOAB: download the publication; DOAB: description of the publication

This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with...