# Hybrid Load Forecasting Using Gaussian Process Regression and Novel Residual Prediction
## Nomenclature  
* R. R.  
* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON] Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information. _Renew. Energy_**2018**, _118_, 357-367. [CrossRef]
* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model. _Energy_**2018**, _160_, 378-387. [CrossRef]
* _[PERSON] et al. (2004)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Forecasting daily urban electric load profiles using artificial neural networks. _Energy Convers. Manag._**2004**, _45_, 2879-2900. [CrossRef]
* _[PERSON] et al. (2006)_ [PERSON]; [PERSON]; [PERSON] Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market. _Energy Convers. Manag._**2006**, _47_, 2128-2142. [CrossRef]
* _[PERSON] (2009)_ [PERSON]; [PERSON] Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique. _Energy Convers. Manag._**2009**, _50_, 2976-2982. [CrossRef]
* _[PERSON] (2019)_ [PERSON]; [PERSON] [PERSON] Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting. _Renew. Energy_**2019**, _136_, 758-768. [CrossRef]
* _[PERSON] et al. (2017)_ [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Very short term load forecasting of a distribution system with high PV penetration. _Renew. Energy_**2017**, _106_, 142-148. [CrossRef]
* _[PERSON] et al. (2012)_ [PERSON]; [PERSON]; [PERSON] Innovative Second-Generation Wavelets Construction with Recurrent Neural Networks for Solar Radiation Forecasting. _IEEE Trans. Neural Netw. Learn. Syst._**2012**, _23_, 1805-1815. [CrossRef]
* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON] PV power forecasting using an integrated GA-PSO-ANFIS approach and Gaussian process regression based feature selection strategy. _CSEE J. Power Energy Syst._**2018**, \(4\), 210-218. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON] Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines. _Knowl. Based Syst._**2019**, _163_, 159-173. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON] A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast. _Renew. Energy_**2019**, _140_, 124-139. [CrossRef]
* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization. _IEEE Trans. Smart Grid_**2018**, \(9\), 3341-3350. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON] Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms. _IEEE Access_**2019**, \(7\), 82584-82598. [CrossRef]
* _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Gaussian process regression based hybrid approach for short-term wind speed prediction. _Energy Convers. Manag._**2016**, _126_, 1084-1092. [CrossRef]
* _Transelectrica (2020)_ Transelectrica. Available online: [[http://transelectrica.ro/en/web/tel/despre-no11](http://transelectrica.ro/en/web/tel/despre-no11)]([http://transelectrica.ro/en/web/tel/despre-no11](http://transelectrica.ro/en/web/tel/despre-no11)) (accessed on 1 May 2020). * _R. P. Ltd. Weather_in_the_World. Available online: [[https://rp5.ru/Weather_archive_in_Bucharest_Filaret](https://rp5.ru/Weather_archive_in_Bucharest_Filaret)]([https://rp5.ru/Weather_archive_in_Bucharest_Filaret](https://rp5.ru/Weather_archive_in_Bucharest_Filaret)) (accessed on 1 May 2020). * _[PERSON] (2013)_ [PERSON] Empirical wavelet transform. _IEEE Trans. Signal Process._**2013**, _61_, 3999-4010. [CrossRef]
* _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep belief network based electricity load forecasting: An analysis of Macedonian case.