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...
Сохранить в:
| Формат: | Online |
|---|---|
| Язык: | английский |
| Опубликовано: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
|
| Предметы: | |
| Online-ссылка: | ONIX_20220621_9783036542188_83 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
|
| _version_ | 1869520806493552640 |
|---|---|
| collection | Directory of Open Access Books |
| description | 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 | Online |
| id | doab-20.500.12854ir-84505 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-845052024-03-28T03:32:39Z Advanced Methods of Power Load Forecasting García-Díaz, J. Carlos Trull, Óscar 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 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. 2022-06-21T08:39:03Z 2022-06-21T08:39:03Z 2022 book ONIX_20220621_9783036542188_83 9783036542188 9783036542171 https://directory.doabooks.org/handle/20.500.12854/84505 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/5489 https://mdpi.com/books/pdfview/book/5489 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-4217-1 10.3390/books978-3-0365-4217-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036542188 9783036542171 128 Basel open access |
| spellingShingle | 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 Advanced Methods of Power Load Forecasting |
| title | Advanced Methods of Power Load Forecasting |
| title_full | Advanced Methods of Power Load Forecasting |
| title_fullStr | Advanced Methods of Power Load Forecasting |
| title_full_unstemmed | Advanced Methods of Power Load Forecasting |
| title_short | Advanced Methods of Power Load Forecasting |
| title_sort | advanced methods of power load forecasting |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20220621_9783036542188_83 |