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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:
Machine Learning
Forecast
Artificial Neural Network
Short-term Load Forecasting
Prophet Model
Deep Learning
Irregular
Time Series
Deep Neural Network
Short-term Load Forecast
Lstm
Encoder Decoder
Multi-layer Stacked
Power System
Research & Information: General / Bicssc
Load
Galvanizing
Physics / Bicssc
Bidirectional Long Short-term Memory
Recurrent Neural Network
Cnn
Long-term Forecasting
Peak Load
Short-term Electrical Load Forecasting
Statistical Analysis
Parameters Tuning
Demand
Online Training
Dims
Holt–winters Model
Neural Network
Attention
Multiple Seasonality
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
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.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (128 p.)
ISBN:9783036542188
9783036542171
books978-3-0365-4217-1

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