Interval forecasting of renewable power generation
With the development of renewable energy power generation industry, effective prediction of renewable energy generation is an important issue that modern power grids are facing. Solar power generation is an important part of renewable energy generation. In this project, solar incident radiation (...
محفوظ في:
المؤلف الرئيسي: | |
---|---|
مؤلفون آخرون: | |
التنسيق: | Theses and Dissertations |
اللغة: | English |
منشور في: |
2019
|
الموضوعات: | |
الوصول للمادة أونلاين: | http://hdl.handle.net/10356/78411 |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
الملخص: | With the development of renewable energy power generation industry, effective
prediction of renewable energy generation is an important issue that modern power grids
are facing. Solar power generation is an important part of renewable energy generation.
In this project, solar incident radiation (SIR) is used as training and test data for research.
By using long short term memory (LSTM) to train network parameters, the results of
point forecasting are obtained which is then converted into prediction intervals by quantile regression method. Considering the uncertainty of SIR, the prediction interval is
more effective than the conventional point forecasting results. Various LSTM framings
are used in this project for comparison and analysis. The conclusions have a guiding role
in solar power generation prediction |
---|