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The Artificial Neural Network based Electric Power Demand Forecast using a Season and Weather Informations
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 Title & Authors
The Artificial Neural Network based Electric Power Demand Forecast using a Season and Weather Informations
Kim, Meekyeong; Hong, Chuleui;
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 Abstract
This paper proposes the new electric power demand forecast model which is based on an artificial neural network and considers time and weather factors. Time factors are selected by measuring the autocorrelation coefficients of load demand in summer and winter seasons. Weather factors are selected by using Pearson correlation coefficient The important weather factors are temperature and dew point because the correlation coefficients between these factors and load demand are much higher than those of the other factors such as humidities, air pressures and wind speeds. The experimental results show that the proposed model using time and seasonal weather factors improves the load demand forecasts to a great extent.
 Keywords
전력수요 예측;인공 신경망;온도 민감도;날씨 가중치;
 Language
Korean
 Cited by
1.
하계 전력수요 예측을 위한 딥 러닝 입력 패턴에 관한 연구,신동하;김창복;

한국정보기술학회논문지, 2016. vol.14. 11, pp.127-134 crossref(new window)
 References
1.
Bureau of economic analysis, U.S. Department of commerce (2013). Retrieved from http://www.bea.gov.

2.
M. Buhari, S. Adamu, "Short - Term Load Forecasting Using Artificial Neural Network," in Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 1, pp. 806-811, 2012.

3.
F. Mosalman, A. Mosalman, H.M. Yazdi, M.M. Yazdi, "One day-ahead load forecasting by artificial neural network," in Scientific Research and Essays, vol. 6, pp. 2795-2799, 2011.

4.
K. Kim, "Fuzzy Expert System for Short-Term Load Forecasting Concerning Changes in Temperature", The Confernece of IEIE, 1995. 1.

5.
PJM.: Manual 19: Load Forecasting and Analysis Date. Prepared by Resource Adequacy Planning, 2013.

6.
J. L. Mathieu, P. N. Price, S. Kiliccote, M. A. Piette, "Quantifying Changes in Building Electricity Use, with Application to Demand Response," Smart Grid IEEE Transactions, vol. 2, pp.507-518, 2011. crossref(new window)

7.
Bureau of economic analysis. U.S. Department of commerce, http://www.bea.gov, 2013.

8.
S. Ha, K. Song and H. Kim, "Short-Term Load Forecasting Using Neural Networks and the Sensitivity of Temperatures in the Summer Season", The Transactions of KIEE, Vol. 54A, No. 6, pp. 259-266, 2005.

9.
J. Han and J. Baek, "The Load Forecasting in Summer Considering Day Factor", The Transactions of KAIS, Vol. 11, No. 8, pp. 2793-2800, 2010.

10.
Y. Park and B. Wang, "Neuro-Fuzzy Model based Electrical Load Forecasting System", The Transactions of KIIS, Vol. 14, No. 5, pp. 553-538, 2004.

11.
J. Park and K. Song, "Short-Term Load Forecast for Summer Special Light-Load Period", The Transactions of KIEE, Vol. 62, No. 4, pp. 482-488, 2013.

12.
D. Ortiz-Arroyo, M.K. Skov, and Q. Huynh, "Accurate Electricity Load Forecasting with Artificial Neural Networks," in Proceedings of the International Conference on Computational Intelligence for Modeling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IEEE Press, Vienna, pp. 94-99, 2005.