| Title |
Development of the Hybrid Spatio-Temporal Solar Power Forecasting System based on Probability |
| DOI |
http://dx.doi.org/10.5207/JIEIE.2018.32.10.017 |
| Keywords |
Hybrid Spatio-Temporal Forecasting ; Naive Bayes Classifier ; Solar Power Forecasting |
| Abstract |
As solar power increases rapidly, there is a problem of integration to the power system. Because solar power has a variability due to a variety of external factors, accurate output forecasting of solar power for stable power system must be preceded. In recent years, as electric vehicle charging demand is expected to increase, renewable output forecasting will become more important to reliably power fluctuating demand side. But, since the existing deterministic method limits the representation of the uncertainty of renewable energy, a probabilistic forecasting method should be performed. In order to express the uncertainty of solar power affected by spatial and temporal environment, this thesis proposed a probability based hybrid spatio-temporal forecasting model. Verification of the forecasting model was conducted using output data from actual solar plant in Korea. |