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References

1 
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2 
J. Cho., H. Kim., H. Ryu., Y. Son., S. Choi., 2021, Analysis of Distributed Power Generation Forecasting Model for Power Distribution Planning, The Transactions of the Korean Institute of Electrical Engineers, Vol. 70, No. 9, pp. 1248-1262DOI
3 
F. Pan, H. Zhang, M. Xia, 2009, A Hybrid Time-Series Forecasting Model Using Extreme Learning Machines, 2009 Second International Conference on Intelligent Computation Technology and Automation, pp. 933-936DOI
4 
H. Yiling, H. Shaofeng, 2020, A Short-Term Load Forecasting Model Based on Improved Random Forest Algorithm, 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), pp. 928-931DOI
5 
T. Anwar, 2018, Introduction to Load Forecasting, International Journal of Pure and Applied MathematicsGoogle Search
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K. A. Keitsch, T. Bruckner, 2016, Input data analysis for optimized short term load forecasts, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), pp. 1-6DOI
7 
J. Cheng, 2016, Evaluating the spatial correlations of multi-area load forecasting errors, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), pp. 1-6DOI
8 
C. Alzate, M. Sinn, 2013, Improved Electricity Load Forecasting via Kernel Spectral Clustering of Smart Meters, 2013 IEEE 13th International Conference on Data Mining, pp. 943-948DOI
9 
J. Chen, T. Li, Y. Zou, G. Wang, H. Ye, F. Lv, 2019, An Ensemble Feature Selection Method for Short-Term Electrical Load Forecasting, 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), pp. 1429-1432DOI
10 
Care, Ridwan Aldila Melania Frisky, Sugeng Subagio Bambang, 2018, Porous concrete basic property criteria as rigid pavement base layer in indonesia., MATEC Web of Conferences, Vol. 147, No. EDP SciencesDOI