Advanced Methods of Power Load Forecasting

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...

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Bibliographic Details
Main Author: García-Díaz, J. Carlos
Other Authors: Trull, Óscar
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Cnn
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
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100 1 |a García-Díaz, J. Carlos 
245 0 0 |a Advanced Methods of Power Load Forecasting  |h Elektronische Ressource 
260 |a Basel  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2022 
300 |a 1 electronic resource (128 p.) 
653 |a machine learning 
653 |a forecast 
653 |a Artificial Neural Network 
653 |a short-term load forecasting 
653 |a prophet model 
653 |a deep learning 
653 |a irregular 
653 |a time series 
653 |a deep neural network 
653 |a short-term load forecast 
653 |a LSTM 
653 |a encoder decoder 
653 |a Prophet model 
653 |a multi-layer stacked 
653 |a power system 
653 |a load 
653 |a Holt-Winters model 
653 |a galvanizing 
653 |a Physics / bicssc 
653 |a bidirectional long short-term memory 
653 |a recurrent neural network 
653 |a CNN 
653 |a long-term forecasting 
653 |a peak load 
653 |a short-term electrical load forecasting 
653 |a statistical analysis 
653 |a parameters tuning 
653 |a demand 
653 |a online training 
653 |a DIMS 
653 |a Research and information: general / bicssc 
653 |a neural network 
653 |a attention 
653 |a multiple seasonality 
700 1 |a Trull, Óscar 
700 1 |a García-Díaz, J. Carlos 
700 1 |a Trull, Óscar 
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520 |a 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.