참고문헌 (19)

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2. K. H. Kim, J. H. Moon, J. W. Park, and E. J Hwang, "Prediction of Household Power Consumption Based on Random Forest", in Proceedings of the KIEE Conference, The Korean Institute of Electrical Engineers, pp. 1502-1503, Jul. 2017. 

3. 김미경, 홍철의. 계절 및 날씨 정보를 이용한 인공신경망 기반 전력수요 예측 알고리즘 개발. Journal of the Institute of Electronics and Information Engineers = 전자공학회논문지, vol.53, no.1, 71-78.

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4. Shin, Dong-Ha, Kim, Chang-Bok. A Study on Deep Learning Input Pattern for Summer Power Demand Prediction. 韓國情報技術學會論文誌 = Journal of Korean institute of information technology, vol.14, no.11, 127-.

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5. Ahn, Jun-Young, Park, Sang-Min, Kim, Chang-Bok. A Study on Neural Network Model for Winter Electric Power Demand Prediction. 韓國情報技術學會論文誌 = Journal of Korean institute of information technology, vol.15, no.9, 1-9.

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6. Nam, Young-Jin, Jo, Ha-Hyun. Modeling of Artificial Neural Network Based on Big Data for the Prediction of Hourly Load in Summer Weekdays. 照明·電氣設備學會論文誌 = Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, vol.33, no.12, 49-58.

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7. Ceperic, Ervin, Ceperic, Vladimir, Baric, Adrijan. A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines. IEEE transactions on power systems : a publication of the Power Engineering Society, vol.28, no.4, 4356-4364.

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8. Y. Y. Cheng, P. P. Chan, and Z. W. Qiu, "Random forest based ensemble system for short term load forecasting", In 2012 International Conference on Machine Learning and Cybernetics, Vol. 1, pp. 52-56, Jul. 2012. 

9. J. Huo, T. Shi, and J. Chang, "Comparison of Random Forest and SVM for electrical short-term load forecast with different data sources", In 2016 7th IEEE International conference on software engineering and service science (ICSESS), Beijing, China, pp. 1077-1080, Aug. 2016. 

10. Tanıdır, Özgür, Tör, Osman Bülent. ACCURACY OF ANN BASED DAY-AHEAD LOAD FORECASTING IN TURKISH POWER SYSTEM: DEGRADING AND IMPROVING FACTORS. Neural network world : international journal on neural and mass-parallel computing and information systems, vol.25, no.4, 443-456.

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11. Chen, Yongbao, Xu, Peng, Chu, Yiyi, Li, Weilin, Wu, Yuntao, Ni, Lizhou, Bao, Yi, Wang, Kun. Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied energy, vol.195, 659-670.

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12. Hong, W.C.. Electric load forecasting by support vector model. Applied mathematical modelling, vol.33, no.5, 2444-2454.

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14. 10.1007/978-3-319-11310-4_71 

    

15. Han, Te, Jiang, Dongxiang, Zhao, Qi, Wang, Lei, Yin, Kai. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control, vol.40, no.8, 2681-2693.

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16. Hippert, H.S., Pedreira, C.E., Souza, R.C.. Neural networks for short-term load forecasting: a review and evaluation. IEEE transactions on power systems : a publication of the Power Engineering Society, vol.16, no.1, 44-55.

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17. Yalcinoz, T., Eminoglu, U.. Short term and medium term power distribution load forecasting by neural networks. Energy conversion and management, vol.46, no.9, 1393-1405.

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19. Kim, Byung Joo. Improved Deep Learning Algorithm. Journal of advanced information technology and convergence, vol.8, no.2, 119-127.

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