Solar Radiation Forecasting Based on the Hybrid CNN-CatBoost Model
Author:
Affiliation:
1. Department of Applied Statistics, Chung-Ang University, Seoul, South Korea
2. Department of Short-Term Demand Forecasting, Korea Power Exchange, Naju, South Korea
Funder
Korea Power eXchange Research Grants in 2023
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10040681.pdf?arnumber=10040681
Reference30 articles.
1. Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons
2. Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China
3. Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models
4. Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms
5. A study on solar irradiance forecasting with weather variables;kim;Korean Journal of Applied Statistics,2017
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