- Volume 8 Issue 1
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Forecasting of Electricity Demand for Fishing Industry Based on Genetic Algorithm approach
유전자 알고리즘에 기반한 수산업 전력 수요 예측에 관한 연구
- Received : 2016.11.03
- Accepted : 2017.01.20
- Published : 2017.01.28
Energy is a vital resource for the economic growth and the social development for any country. As the industry becomes more sophisticated and the economy more grows, the electricity demand is increasing. So forecasting electricity demand is an important for electricity suppliers. Forecasting electricity demand makes it possible to distribute electricity demand. As the market for Negawatt market began to grow in Korea from 2014, the prediction of electricity consumption demand becomes more important. Moreover, power consumption forecasting provides a way for demand management to be directly or indirectly participated by consumers in the electricity market. We use Genetic Algorithms to predict the energy demand of the fishing industry in Jeju Island by using GDP, per capita gross national income, value add, and domestic electricity consumption from 1999 to 2011. Genetic Algorithm is useful for finding optimal solutions in various fields. In this paper, genetic algorithm finds optimal parameters. The objective is to find the optimal value of the coefficients used to predict the electricity demand and to minimize the error rate between the predicted value and the actual power consumption values.
Supported by : 미래창조과학부
- S. Y. Kim, H. W. Jung, J. D. Park, S. M. Baek, W. S. KIM, K. H. Chon and K. B. Song, "Weekly Maximum Electric Load Forecasting for 104 Weeks by Seasonal ARIMA Model", Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, Vol. 28, No. 1, pp. 50-56, 2014.. https://doi.org/10.5207/JIEIE.2014.28.1.050
- H. W. Jung and K. B. Song. "Daily Maximum Electric Load Forecasting for the Next 4 Weeks for Power System Maintenance and Operation", The Transactions of the Korean Institute of Electrical Engineers, Vol. 63, No. 11, pp. 1497-1502, 2014.. https://doi.org/10.5370/KIEE.2014.63.11.1497
- H. Ceylan, H. Ozturk, "Estimation energy demand of Turkey based on economic indicators using genetic algorithm approach", Energy Conversion and Management, pp. 2525-2537, 2004.
- H. Ozturk, H. Ceylan, O. E. Canyurt, A. Hepbasli, "Electricity estimation using genetic approach : a case study of Turkey", Energy 30, pp. 1003-1012, 2003.
- Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Wil L. Kling, "Comparison of Machine Learning Methods for Estimating Energy Cinsumption in Building", PMAPS, 2014.
- S. Wong, K. K. Wan and T. N. Lam, "Artificial neural networks for energy analysis of office buildings with daylighting", Applied Energy, Vol. 87, No. 2, pp. 5510557, 2010. https://doi.org/10.1016/j.apenergy.2009.06.028
- Goldberg DE, Genetic algorithm in search, optimization and machine learning, Adison wesley, 1989.
- T. J. VanderNoot, I. Abrahams, "The use of genetic algorithms in non-linear regression of emmittance data", Journal of Electro Analytical Chemistry, Vol. 448, Issue 1, pp. 17-23, 1998. https://doi.org/10.1016/S0022-0728(97)00593-7
- S. N. Sivanandam, S. N. Deepa, "Introduction to Genetic Algorithm", pp. 80-92, 2007.
- D. Bunning, M. Sun, "Genetic algorithm for constrained global optimization in continuous variables", Applied Mathematics & Computation, pp. 604-636, 2005.
- H. Hasheminia, S. T. Akhavan Niaki, "A genetic algorithm approach to fit the best regression/econometric model among the candidates", Applied Mathematics & Computation, 2006.
- A. Sadeghi, "Economics faculty, Electricity consumption forecasting using econometrics methods", 2003.
- M. Zamani, economics faculty, Teheran University, Electricity demand function estimation in different economic sectors, 1998.
- A. Tang, C. Quek and G. Ng, "GA-TSKfnn : Parameters tuning of fuzzy neural network using genetic algorithms", Expert Systems with Applications, pp. 769-781, 2005.
- Yu SH, Choi JY, "The causal relationship between energy and GNP : an international comparison", Journal of Energy, pp. 249-272, 1985.