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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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Gardado en:
Formato: Online
Idioma:inglés
Publicado: MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
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
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
thema EDItEUR::P Mathematics and Science::PH Physics
Acceso en liña:ONIX_20220621_9783036542188_83
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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.

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