Scientific or industry publications using Solcast data

Solcast itself has a research background, and our data is widely used in the scientific community. We salute public researchers, and all those working to advance the state of the art (and science).

NOTE: Solcast provides public researchers with free data credits. If you’re currently a researcher or student at a university or other public research institution, you’ll be eligible for free data credits when you sign up, and you can write to us to request further credits, too.

  • Bean, R., & Khan, H. (2018). Using solar and load predictions in battery scheduling at the residential level. arXiv preprint arXiv:1810.11178. Read publication

  • Bright, Jamie. (2019). Solcast: Validation of a satellite-derived solar irradiance dataset. Solar Energy. 189. 435-449. 10.1016/j.solener.2019.07.086. Read publication

  • Bright, J. M., Killinger, S., & Engerer, N. A. (2019). Data article: Distributed PV power data for three cities in Australia. Journal of Renewable and Sustainable Energy, 11(3), 035504. Read publication

  • Bright, J. M., Killinger, S., Lingfors, D., & Engerer, N. A. (2018). Improved satellite-derived PV power nowcasting using real-time power data from reference PV systems. Solar Energy, 168, 118-139. Read publication

  • Bright, J. M., Killinger, S., Lingfors, D., & Engerer, N. A. (2017). Integration of distributed solar forecasting with distribution network operations in Australia. In ISES Solar World Congress 2017. Read publication

  • Engerer, N. (2017). Forecasting technology: How to stay ahead of the clouds. Ecogeneration, (100), 32. Read publication

  • Ernst, M., & Gooday, J. (2019). Methodology for generating high time resolution typical meteorological year data for accurate photovoltaic energy yield modelling. Solar Energy, 189, 299-306. Read publication

  • Ferreira, J., David, J. M. N., Braga, R., Campos, F., Ströele, V., & de Aguiar, L. (2019, May). Supporting the Collaborative Research through Semantic Data Integration. In 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 319-324). IEEE. Read publication

  • Keerthisinghe, C., Chapman, A. C., & Verbič, G. (2018). PV and Demand Models for a Markov Decision Process Formulation of the Home Energy Management Problem. IEEE Transactions on Industrial Electronics, 66(2), 1424-1433. Read publication

  • Keerthisinghe, C., Chapman, A. C., & Verbič, G. (2018). Energy management of PV-storage systems: Policy approximations using machine learning. IEEE Transactions on Industrial Informatics, 15(1), 257-265. Read publication

  • Killinger, S., Bright, J. M., Lingfors, D., & Engerer, N. A. (2017). Towards a Tuning Method of PV Power Measurements to Balance Systematic Influences. In ISES Solar World Congress 2017, Abu Dhabi, United Arab Emirates, October 29-November 2. Read publication

  • Petrichenko, L., Zemite, L., Sauhats, A., Klementavicius, A., & Grickevics, K. (2019, June). A Comparative Analysis of Supporting Policies for Solar PV systems in the Baltic Countries. In 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1-7). IEEE. Read publication

  • Xia, C., Li, W., Chang, X., Delicato, F., Yang, T., & Zomaya, A. (2018, August). Edge-based energy management for smart homes. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (pp. 849-856). IEEE. Read publication

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