• Title, Summary, Keyword: 네가와트

Search Result 2, Processing Time 0.026 seconds

Forecasting of Electricity Demand for Fishing Industry Based on Genetic Algorithm approach (유전자 알고리즘에 기반한 수산업 전력 수요 예측에 관한 연구)

  • Kim, Heung-Soe;Lee, Sung-Geun
    • Journal of the Korea Convergence Society
    • /
    • v.8 no.1
    • /
    • pp.19-23
    • /
    • 2017
  • 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.