Short-Term Photovoltaic Output Forecasting with Weakly Related Meteorological Data
- DOI
- 10.2991/cisia-15.2015.115How to use a DOI?
- Keywords
- distributed photovoltaic; PV output forecasting; support vector machine; parameter selected
- Abstract
Photovoltaic (PV) output is influenced by many meteorological factors. The significant degree of meteorological data influences the accuracy of forecasting result. This paper proposed a short-term PV output forecasting method while the weather data and PV output data were weak correlated. By analyzing meteorological historical data and PV output historical data, the main factors effecting PV generation were found out by Pearson correlation coefficient. Based on relevant factors, fuzzy clustering analysis method was used to select similar days, and then support vector regression (SVR) forecasting model was built. SVR model has excellent learning ability for small sample. To determine model parameters, a two-step method was proposed. First, using the global search method to determine the value of parameter ? and the appropriate range of kernel parameter p and regularization parameter C, then using self-adaptive differential evolution algorithm to find the optimal p and C, in order to improve the forecast accuracy when parameter ? was selected in large scale. Examples show that the method proposed in this paper has good forecasting ability when the weather data and PV output data are weak correlated.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - C.H Lin AU - Y. Xiao AU - J.G Chen AU - X.K Wen AU - C. Du PY - 2015/06 DA - 2015/06 TI - Short-Term Photovoltaic Output Forecasting with Weakly Related Meteorological Data BT - Proceedings of the International Conference on Computer Information Systems and Industrial Applications PB - Atlantis Press SP - 426 EP - 429 SN - 2352-538X UR - https://doi.org/10.2991/cisia-15.2015.115 DO - 10.2991/cisia-15.2015.115 ID - Lin2015/06 ER -