Both wind and solar power, the two most rapidly expanding types of renewable power, are driven by local weather conditions, in particular wind speed, temperature, and solar irradiance. The ability to forecast these weather data, together with accurate generator models, thus enables wind and solar power production forecasts. Statistical forecasting methods can contribute to short-term forecasting, which plays a particularly important role for preventing negative impacts of large-scale renewables integration in power systems.
The goal of this thesis is to continue to develop an efficient storage and analysis systems for big weather data using the Hadoop stack [1], in particular HBase [2]. The database should be populated with actual weather data from publicly available sources, in particular using a web service offered by the Norwegian Meteorological Institute [3], and should be extended with wind turbine (WT) output and photovoltaic (PV) output data from available sources that are correlated with the weather data. The effort should build on an existing prototype a prior thesis already worked on. Moreover, based on the collected weather data, the thesis should develop a output prediction method, which constructs a time series output based on an auto-regression (AR) model and estimates its parameters by using a support vector machine (SVM) and robust parameter estimation methodologies. The combination of AR and SVM should be applied to predict varying time horizons (e.g., 1hr, 12hrs, 24-hour ahead trends) of PV/WT power output.
| Description of the task | Prerequisites |
|
|
Acquiring skills
A new master level course on ”Energy Informatics” where students will acquire knowledge and skills relevant for this project, will be introduced spring 2017.
References
- Apache Hadoop Video Tutorial. http://www.youtube.com/channel/UCOKFIXDVK-ntuaFMb8IPqcw
- Lars George. HBase – The Definite Guide. O’Reilly. 2011.
- api.met.no. http://api.met.no/#english
- E. Lorenz, T. Scheidsteger, J. Hurka, D. Heinemann, C. Kurz. Regional PV Power Prediction for Improved Grid Integration. Progress in Photovoltaics: Research and Applications (19), pp. 757-771. 2011
- Huang et al. Wind Energy Forecasting: A Review of State-of-the-Art and Recommendations for Better Forecasts. California Renewable Energy Forecasting, Resource Data and Mapping. http://uc-ciee.org/downloads/appendixB.pdf