Study on Photovoltaic Plant Site Selection Models Based on Geographic and Environmental Features
[Objective] To improve the accuracy of global horizontal irradiance （GHI） prediction and completely explore its application value in solar energy resource assessments， such as photovoltaic site selection， this study proposes a GHI prediction model that integrates the temporal convolutional network （...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Power Construction
2025-07-01
|
| Series: | Dianli jianshe |
| Subjects: | |
| Online Access: | https://www.cepc.com.cn/fileup/1000-7229/PDF/1750819589988-883631943.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | [Objective] To improve the accuracy of global horizontal irradiance （GHI） prediction and completely explore its application value in solar energy resource assessments， such as photovoltaic site selection， this study proposes a GHI prediction model that integrates the temporal convolutional network （TCN） with the former architecture. [Methods] To address the presence of anomalies in the GHI data， raw data were first cleaned and preprocessed to eliminate outliers and ensure data quality. Then， the model leveraged the temporal feature extraction capability of the TCN to perform deep representation learning on preprocessed multisource input data， whereas the former network was employed to capture long-term dependencies. A high-precision prediction framework driven by multiple features was constructed by incorporating environmental and geographical parameters into the model input to enhance the overall performance. [Results] Comparative experiments conducted on real-world datasets from multiple regions demonstrated that the proposed TCN-Informer model outperformed mainstream prediction models in terms of mean absolute error， mean absolute percentage error， and root mean square error. Compared with the second-best performing informer model， the proposed model achieved reductions of 24.0%， 23.1%， and 28.5% in the mean absolute error， mean absolute percentage error， and root mean square error， respectively. [Conclusions] The TCN-Informer model exhibited significant advantages in terms of accuracy and robustness for GHI prediction， enabling a more effective capture of temporal variation patterns in solar irradiance. It has a strong engineering application potential and provides solid data support for solar resource evaluation and photovoltaic site planning. |
|---|---|
| ISSN: | 1000-7229 |