.png){kind=icon}
返回
A deep-learning algorithm with two-stage training for solar forecast post-processing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.solener.2024.112504.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Post-processing is a common strategy to boost the quality of irradiance forecasts from numerical weather prediction (NWP). This work approaches this problem from a machine learning perspective and proposes a two-stage optimization training method based on an improved multi-layer extreme learning machine (ML- ELM). In the first stage, the standard ELM network is stacked into ML-ELM using the auto-encoder, and the parameters of the network are trained to calculate the weight matrix between the connection layers. In the second stage, the particle swarm optimization algorithm is employed to further improve the network by optimizing the parameters. In the empirical part of this study, the proposed post-processing method is applied to NWP forecasts issued by the European Centre for Medium-Range Weather Forecasts. The apple-to-applecomparison, pple comparison, which is exceedingly rare in the current research, shows that the two-stage post-processing method is able to outperform not just previously published results obtained through the kernel conditional density estimation, but also those from two other ELM-based methods.
Keyword:
Extreme learning machine
Machine learning
Solar forecasting
Numerical weather prediction
Post-processing
期刊
IF:
6.6
论文数:
1.4W
被引数:
6.2W
机构
引用论文
分享收藏Productivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materials.png)
Desalination
OAAI
DesalinationIF0
OAAI分享收藏A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality太阳预报，其对大气科学的依赖及其对电网整合的影响: 迈向碳中和
PREAI
分享收藏REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation - Validation with a benchmark datasetSOLAR ENERGY
SOLAR ENERGYIF6.6
PREAI
分享收藏A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation确定极限学习机预测水平全球太阳辐射的适用性的比较评估
PREAI
分享收藏
查看更多内容