CAJ | 학술논문

短期电力负荷预测是实现电力系统安全、经济运行的基础，其准确性对电力系统运行的可靠性、经济性都有重要意义。本文在灰色模型、ARIMA模型以及指数平滑族模型的基础上，提出了一种基于以上三种模型的组合预测模型，并用粒子群优化算法对其组合权重系数进行了优化。对澳大利亚新南威尔士州2011年9月实际电力负荷数据进行实例分析，其结果表明本文提出的组合模型确实能够提高短期电力负荷预测的精度。
단기전력부하예측시실현전력계통안전、경제운행적기출，기준학성대전력계통운행적가고성、경제성도유중요의의。본문재회색모형、ARIMA모형이급지수평활족모형적기출상，제출료일충기우이상삼충모형적조합예측모형，병용입자군우화산법대기조합권중계수진행료우화。대오대리아신남위이사주2011년9월실제전력부하수거진행실례분석，기결과표명본문제출적조합모형학실능구제고단기전력부하예측적정도。
The short-term power load forecasting is the foundation of the safe and economic electric power system.Thus,the accuracy of the short-term power load forecasting is significant for the reliability and economy of power system.In this paper,a combined forecasting model based on grey model,ARIMA model and exponential smoothing models is proposed,of which the weight coefficients are optimized by particle swarm optimization algorithm.Then,the results of case study of the electric load data of New South Wales, Australia, in September 2011 show that the proposed combined model can indeed improve the accuracy of the short-term power load forecasting.