References
- Copernicus Atmosphere Monitoring Service. (2021). User guide to the CAMS radiation service (CRS). (PDF) https://atmosphere.copernicus.eu/sites/default/files/2022-01/CAMS2_73_2021SC1_D3.2.1_2021_UserGuide_v1.pdf?utm_source=chatgpt.com
- Deutscher Wetterdienst. (2024). ICON numerical weather prediction model: Forecast data and documentation. https://www.dwd.de/EN/ourservices/nwp_forecast_data/nwp_forecast_data.html
- Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Muñoz-Sabater, J., … & Thépaut, J.-N.(2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
- Sakipova, S. E., Jakovics, A., & Gendelis, S. (2016). The potential of renewable energy sources in Latvia. Latvian Journal of Physics and Technical Sciences, 53(3), 3–14. https://doi.org/10.1515/lpts-2016-0001
- GeoRiga. (2022). LiDAR point cloud (open data). https://georiga.eu/atvertie-dati/lidarpunktu-makonis/
- Latvian Environment, Geology and Meteorology Centre (LVĢMC). (2025). About LVLEGMC. https://videscentrs.lvgmc.lv/lapas/sllc-latvian-environment-geology-and-meteorology-centre
- Wiltink, J. I., Deneke, H., Saint-Drenan, Y.-M., van Heerwaarden, C. C., & Meirink, J. F. (2024). Validating high-resolution global horizontal irradiance retrievals. Atmospheric Measurement Techniques, 17, 6003–6026. https://doi.org/10.5194/amt-17-6003-2024
- Urraca, R., Huld, T., Gracia-Amillo, A., Martínez-de-Pisón, F. J., Kaspar, F., & Sanz-García, A. (2018). Evaluation of GHI estimates from ERA5 and COSMO-REA6 using ground- and satellite-based data over Europe. Solar Energy, 164, 339–354. https://doi.org/10.1016/j.solener.2018.02.059
- Redweik, P., Catita, C., & Brito, M. C. (2013). Solar energy potential on roofs and façades in an urban landscape. Solar Energy, 97, 332–341. https://doi.org/10.1016/j.solener.2013.08.036
- Bill, A., Mohajeri, N., & Scartezzini, J.-L. (2016). 3D model for solar energy potential on buildings from urban LiDAR data. UDMV 2016, 51–56. https://doi.org/10.2312/udmv.20161420
- Jakica, N. (2018). State-of-the-art review of solar design tools and methods for urban microclimate studies. Renewable and Sustainable Energy Reviews, 81(Part 1), 1296–1328. https://doi.org/10.1016/j.rser.2017.05.080
- Kazemian, A., & Xiang, C. (2025). Large-scale analysis of photovoltaic, photovoltaic-thermal, and solar thermal systems in high-density urban environments. Applied Energy, 401(Part B), 126765. https://doi.org/10.1016/j.apenergy.2025.126765
- Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
- de Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38–48. https://doi.org/10.1016/j.neucom.2015.12.114
- Joshi, B.; Kay, M.; Copper, J. K.; & Sproul, A. B. (2019). Evaluation of solar irradiance forecasting skills of the Australian Bureau of Meteorology’s ACCESS models. Solar Energy, 188, 386–402. https://doi.org/10.1016/j.solener.2019.06.007
- Lohmann, G. M., & Monahan, A. H. (2018). Effects of temporal averaging on short-term irradiance variability under mixed sky conditions. Atmospheric Measurement Techniques, 11, 3131–3144. https://doi.org/10.5194/amt-11-3131-2018
- Gueymard, C. A. (2018). A review of validation methodologies and statistical performance indicators for modelled solar radiation data. Solar Energy, 169, 260–277. https://doi.org/10.1016/j.solener.2018.04.067
- Draper, N. R., & Smith, H. (1998). Applied Regression Analysis (3rd ed.). Wiley.
- Urbich, I., Bendix, J., & Müller, R. (2020). Development of a seamless forecast for solar radiation using ANAKLIM++. Remote Sensing, 12(21), 3672. https://doi.org/10.3390/rs12213672