Advanced Methods of Power Load Forecasting
García-Díaz, J. Carlos
Advanced Methods of Power Load Forecasting - Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (128 p.)
Open Access
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.
Creative Commons
English
9783036542188 9783036542171
Искусственный интеллект
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
004
Advanced Methods of Power Load Forecasting - Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (128 p.)
Open Access
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.
Creative Commons
English
9783036542188 9783036542171
Искусственный интеллект
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
004