Renewable Energy Forecast Using Machine Learning

Renewable energy forecasting becomes increasingly important as the contribution of solar/wind power production to the electrical power grid constantly increases.[1] Significant improvement in forecasting accuracy has been demonstrated by developing more sophisticated solar irradiance forecasting models using statistics and/or numerical weather predictions.[2] In this presentation, we report the development of a machine-learning based multi-model blending approach for statistically combing multiple meteorological models for improving the accuracy of solar power forecast. The system leverages upon multiple existing physical models for prediction including numerous atmospheric and cloud prediction models based on sky camera and satellite imagery as well as numerical weather prediction (NWP) products. Fig 1 shows an architectural view of this forecast system. Results using the system show over 30% improvement in solar irradiance/power forecast accuracy compared to forecast based on the best individual model.[3] A typical forecast time series plot is shown in Fig 2 together with measurement data and power from clear sky conditions. We also demonstrated that in addition to the parameters to be predicted (such as solar irradiance and power), including additional state parameters which collectively define a weather situation as machine learning input provides further enhanced accuracy for the machine learning result. This methodology has also been applied to wind power generation forecast and similar improvement in accuracy has been demonstrated.

The work is partially supported by Department of Energy SunShot Initiative contract #DE-EE0006017.

[1] R. Margolis,C. Coggeshall, J. Zuboy, “Integration of solar into the U.S. electric power system”, Chap 6 in SunShot Vision Study, US Department of Energy, http://energy.gov/eere/sunshot/sunshot-vision-study(2012)

[2] M. Dagne, M. David, P. Lauret, J. Boland, and N. Schmutz, “Review of Solar Irradiance Forecasting Methods and a Proposition for Small-Scale Insular Grids”, Renewable and Sustainable Energy Reviews, 27, p65 (2013)

[3] S. Lu, Y. Hwang, I. Khabibrakhmanov, H. Dang, T. van Kessel, F. Marianno, X. Shao, H. F. Hamann, “Machine Learning Based Multi-Model Blending for Enhancing Renewable Energy Forecasting”, American Meteorological Society Annual Meeting Abstract J6.4, Phoenix, AZ, January 4-8, 2015.