A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning
Accurate wind power forecasting is crucial for optimizing grid scheduling and improving wind power utilization. However, real-world wind power time series exhibit dynamic statistical properties, such as changing mean and variance over time, which make it difficult for models to apply observed patter...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/3/580 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850199371559206912 |
|---|---|
| author | Yan Chen Miaolin Yu Haochong Wei Huanxing Qi Yiming Qin Xiaochun Hu Rongxing Jiang |
| author_facet | Yan Chen Miaolin Yu Haochong Wei Huanxing Qi Yiming Qin Xiaochun Hu Rongxing Jiang |
| author_sort | Yan Chen |
| collection | DOAJ |
| description | Accurate wind power forecasting is crucial for optimizing grid scheduling and improving wind power utilization. However, real-world wind power time series exhibit dynamic statistical properties, such as changing mean and variance over time, which make it difficult for models to apply observed patterns from the past to the future. Additionally, the execution speed and high computational resource demands of complex prediction models make them difficult to deploy on edge computing nodes such as wind farms. To address these issues, this paper explores the potential of linear models for wind power forecasting and constructs NFLM, a linear, lightweight, short-term wind power forecasting model that is more adapted to the characteristics of wind power data. The model captures both short-term and long-term sequence variations through continuous and interval sampling. To mitigate the interference of dynamic features, we propose a normalization feature learning block (NFLBlock) as the core component of NFLM for processing sequences. This module normalizes input data and uses a stacked multilayer perceptron to extract cross-temporal and cross-dimensional dependencies. Experiments with data from two real wind farms in Guangxi, China, showed that compared with other advanced wind power forecasting methods, the MSE of NFLM in the 24-step ahead forecasting of the two wind farms is respectively reduced by 23.88% and 21.03%, and the floating-point operations (FLOPs) and parameter count only require 36.366 M and 0.59 M, respectively. The results show that NFLM can achieve good prediction accuracy with fewer computing resources. |
| format | Article |
| id | doaj-art-e1af174101ed4c5aae9fa53af0b57b07 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e1af174101ed4c5aae9fa53af0b57b072025-08-20T02:12:38ZengMDPI AGEnergies1996-10732025-01-0118358010.3390/en18030580A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature LearningYan Chen0Miaolin Yu1Haochong Wei2Huanxing Qi3Yiming Qin4Xiaochun Hu5Rongxing Jiang6School of Business, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaDispatching Control Center of Guangxi Power Grid, Nanning 530004, ChinaDispatching Control Center of Guangxi Power Grid, Nanning 530004, ChinaGuangxi Key Laboratory of Big Data in Finance and Economics, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaAccurate wind power forecasting is crucial for optimizing grid scheduling and improving wind power utilization. However, real-world wind power time series exhibit dynamic statistical properties, such as changing mean and variance over time, which make it difficult for models to apply observed patterns from the past to the future. Additionally, the execution speed and high computational resource demands of complex prediction models make them difficult to deploy on edge computing nodes such as wind farms. To address these issues, this paper explores the potential of linear models for wind power forecasting and constructs NFLM, a linear, lightweight, short-term wind power forecasting model that is more adapted to the characteristics of wind power data. The model captures both short-term and long-term sequence variations through continuous and interval sampling. To mitigate the interference of dynamic features, we propose a normalization feature learning block (NFLBlock) as the core component of NFLM for processing sequences. This module normalizes input data and uses a stacked multilayer perceptron to extract cross-temporal and cross-dimensional dependencies. Experiments with data from two real wind farms in Guangxi, China, showed that compared with other advanced wind power forecasting methods, the MSE of NFLM in the 24-step ahead forecasting of the two wind farms is respectively reduced by 23.88% and 21.03%, and the floating-point operations (FLOPs) and parameter count only require 36.366 M and 0.59 M, respectively. The results show that NFLM can achieve good prediction accuracy with fewer computing resources.https://www.mdpi.com/1996-1073/18/3/580wind power forecastingdeep learningmulti-layer perceptrondynamic featureslightweight modeling |
| spellingShingle | Yan Chen Miaolin Yu Haochong Wei Huanxing Qi Yiming Qin Xiaochun Hu Rongxing Jiang A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning Energies wind power forecasting deep learning multi-layer perceptron dynamic features lightweight modeling |
| title | A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning |
| title_full | A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning |
| title_fullStr | A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning |
| title_full_unstemmed | A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning |
| title_short | A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning |
| title_sort | lightweight framework for rapid response to short term forecasting of wind farms using dual scale modeling and normalized feature learning |
| topic | wind power forecasting deep learning multi-layer perceptron dynamic features lightweight modeling |
| url | https://www.mdpi.com/1996-1073/18/3/580 |
| work_keys_str_mv | AT yanchen alightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT miaolinyu alightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT haochongwei alightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT huanxingqi alightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT yimingqin alightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT xiaochunhu alightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT rongxingjiang alightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT yanchen lightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT miaolinyu lightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT haochongwei lightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT huanxingqi lightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT yimingqin lightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT xiaochunhu lightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning AT rongxingjiang lightweightframeworkforrapidresponsetoshorttermforecastingofwindfarmsusingdualscalemodelingandnormalizedfeaturelearning |