A New Prediction Model for Power Consumption with Local Weather Information

지역 기상 정보를 활용한 단기 전력 수요 예측 모델

  • 탁해성 (부산대학교 전기전자컴퓨터공학과) ;
  • 김태용 (부산대학교 전기전자컴퓨터공학과) ;
  • 조환규 (부산대학교 전기전자컴퓨터공학과) ;
  • 김희제 (부산대학교 전기전자컴퓨터공학과)
  • Received : 2016.09.21
  • Accepted : 2016.11.17
  • Published : 2016.11.28


Much of the information is stored as data, research has been activated for analyzing the data and predicting the special circumstances. In the case of power data, the studies, such as research of renewable energy utilization, power prediction depending on site characteristics, smart grid, and micro-grid, is actively in progress. In this paper, we propose a power prediction model using the substation environment data. In this case, we try to verify the power prediction result to reflect the multiple arguments on the power and weather data, rather than a simple power data. The validation process is the effect of multiple factors compared to other two methods, one of power prediction result considering power data and the other result using power pattern data that have been made in the similar weather data. Our system shows that it can achieve max prediction error of less than 15%.


Power Prediction;Support Vector Regression;Short-term Prediction


Supported by : 한국연구재단


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