Reprint

Advanced Methods of Power Load Forecasting

Edited by
May 2022
128 pages
  • ISBN978-3-0365-4218-8 (Hardback)
  • ISBN978-3-0365-4217-1 (PDF)

This is a Reprint of the Special Issue Advanced Methods of Power Load Forecasting that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

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 a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
Prophet model; Holt–Winters model; long-term forecasting; peak load; prophet model; multiple seasonality; Holt–Winters model; long-term forecasting; 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