Interval forecasting of renewable power generation

• Main Author: Zhang, Hanjie
• Other Authors: Xu Yan
• Format: Theses and Dissertations
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/78410
• Institution: Nanyang Technological University

With the great development of renewable power generation, forecasting of renewable power generation output is significant for modern power grids. Solar power generation, which is the important component of renewable power generation, is selected as the analysis direction in this project. But accurately predicting solar energy is critical for grid operators to ensure energy management from multiple sources without compromising stability and is critical to arranging plant maintenance cycles and avoiding power imbalance costs for PV plant owners. It is evident that meteorological data like solar incoming radiance (SIR) is more readily available than the historical PV power output series with hourly samples. So that the solar incoming radiation, the main factor of uncertainty for solar power generation, is used as training and testing data. By using the forecast accuracy of extreme learning machine (ELM) and gradient descent, the optimal result of point forecasting is provided, which is the initial data for interval forecasting. The main process is the conversion from the value of point forecasting to prediction intervals (PIs) with the help of PI formation of SIR. This method has good reference value for other renewable power generation with high uncertainty.