Advanced Methods of Power Load Forecasting
Material type:
ArticleLanguage: English Publication details: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022Description: 1 electronic resource (128 p.)ISBN: - 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
| Item type | Current library | Collection | Shelving location | Call number | Status | Notes | Date due | Barcode |
|---|---|---|---|---|---|---|---|---|
| Electronic edition | Bucheon University Library | Computers | MDPI books | 004 A20 | Not for loan | Смотреть (pdf) | 1010619 |
Open Access star Unrestricted online 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 https://creativecommons.org/licenses/by/4.0/ cc
English
There are no comments on this title.