Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems
Problem
Short-term forecasting of clustered hybrid renewable energy systems (wind, photovoltaic, concentrating solar power) is difficult due to spatial and temporal uncertainties.
Why it matters
Accurate forecasts of variable renewable generation are needed for reliable operation and integration of clean power.
What the AI did
An LSTM network was trained on spatial panel data combining historical power and meteorological data from CSP, wind and PV plants to forecast the clustered generation.
Results
Case study indicated cluster power uncertainty tends to weaken due to partial controllability; no numeric forecast accuracy stated in provided abstract
Impact
The method was shown in a case study to exploit correlations among generation types to improve hybrid forecasting.
Record
| Venue | Sustainability |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Simian Pang, Zixuan Zheng, Fan Luo, Xianyong Xiao, Lanlan Xu |
| Citations | 19 (OpenAlex, at import time) |
| References | 37 |
| Model or method | LSTM (long short-term memory network) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/su13126681 |
| Open access | gold |
| Keywords | LSTM, renewable energy forecasting, wind power, photovoltaic, concentrating solar power |
Importance rating
real but narrower advance
Renewable cluster forecasting aids clean power integration, a significant need, but abstract only shows one real case in Northwest China without quantified scale or accuracy.
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