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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2504.08008 |
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| _version_ | 1866910908883664896 |
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| author | Hwang, Jeonggyu |
| author_facet | Hwang, Jeonggyu |
| contents | This study proposes an approximate model to estimate the solar radiation spectrum intensity in Seoul, Republic of Korea, for the year 2024, aiming to analyze optimal conditions related to energy generation. Since the solar radiation spectrum varies with atmospheric conditions, accurately predicting it typically requires complex spectral radiation models. However, such models entail high computational costs, hindering real-time application. To address this, this study introduces a simplified approximation model using only direct normal irradiance (DNI) among real-time meteorological elements, employing linear scaling of the standard spectrum (ASTM G-173). This model first estimates DNI using global horizontal irradiance (GHI) and solar position information (such as zenith angle), then linearly adjusts the standard spectrum to compute real-time spectrum intensity. The model approximates realistic DNI values by correcting various meteorological parameters, including zenith angle, cloud cover, and visibility. The analysis shows that GHI exhibits stable seasonal patterns, peaking in summer and minimizing in winter. In contrast, DNI demonstrates significant temporal variability and frequent abnormal peaks (e.g., exceeding 9,000 W/m^2), highlighting the importance of data refinement and anomaly detection in predicting energy generation. In conclusion, GHI is suitable for general photovoltaic analyses, whereas DNI is crucial for direct-beam sensitive systems like concentrated solar power (CSP), requiring meticulous data quality management. Future research should focus on identifying the causes of DNI anomalies and developing real-time quality control algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimation of Solar Spectral Irradiance Using Meteorological Data and Analysis of Optimal Conditions for Solar Power Generation Hwang, Jeonggyu Signal Processing This study proposes an approximate model to estimate the solar radiation spectrum intensity in Seoul, Republic of Korea, for the year 2024, aiming to analyze optimal conditions related to energy generation. Since the solar radiation spectrum varies with atmospheric conditions, accurately predicting it typically requires complex spectral radiation models. However, such models entail high computational costs, hindering real-time application. To address this, this study introduces a simplified approximation model using only direct normal irradiance (DNI) among real-time meteorological elements, employing linear scaling of the standard spectrum (ASTM G-173). This model first estimates DNI using global horizontal irradiance (GHI) and solar position information (such as zenith angle), then linearly adjusts the standard spectrum to compute real-time spectrum intensity. The model approximates realistic DNI values by correcting various meteorological parameters, including zenith angle, cloud cover, and visibility. The analysis shows that GHI exhibits stable seasonal patterns, peaking in summer and minimizing in winter. In contrast, DNI demonstrates significant temporal variability and frequent abnormal peaks (e.g., exceeding 9,000 W/m^2), highlighting the importance of data refinement and anomaly detection in predicting energy generation. In conclusion, GHI is suitable for general photovoltaic analyses, whereas DNI is crucial for direct-beam sensitive systems like concentrated solar power (CSP), requiring meticulous data quality management. Future research should focus on identifying the causes of DNI anomalies and developing real-time quality control algorithms. |
| title | Estimation of Solar Spectral Irradiance Using Meteorological Data and Analysis of Optimal Conditions for Solar Power Generation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.08008 |