Spatial-Temporal Solar Power Forecast through Use of Gaussian Conditional Random Fields Conference Paper

abstract

• 2016 IEEE. This paper introduces an application of the Gaussian Conditional Random Fields (GCRF) model for forecasting the solar power in electricity grids. The introduced forecasting technique is capable of modeling both the spatial and temporal correlations of various solar generation stations. It will be demonstrated in this paper how the suggested solution can significantly improve the forecast accuracy compared to the conventional forecasting models such as the persistent (PSS) model and the autoregressive with exogenous input (ARX) model. Besides, the GCRF model outperforms the other two models under the scenarios with unavailable or missing data. The suggested probabilistic model of the GCRF can also help better managing the existing uncertainties of the solar generations. The numerical experiments are conducted through which the effectiveness of the proposed approach is validated.

name of conference

• 2016 IEEE Power and Energy Society General Meeting (PESGM)

authors

• Kezunovic, Mladen

published proceedings

• 2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM)

author list (cited authors)

• Zhang, B., Dehghanian, P., & Kezunovic, M.

citation count

• 17

complete list of authors

• Zhang, Bei||Dehghanian, Payman||Kezunovic, Mladen

publication date

• July 2016

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Power and Energy Society General Meeting  Journal

keywords

• Forecast
• Gaussian Conditional Random Fields
• Solar Power
• Spatial-temporal

Digital Object Identifier (DOI)

• 10.1109/pesgm.2016.7741503

International Standard Book Number (ISBN) 13

• 9781509041688

start page

• 1

end page

• 5

volume

• 2016-November

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fpesgm.2016.7741503