CAJ | 학술논문

随着风电的大规模接入电网，对风电功率未来出力的把握显得尤为重要，而风电功率预测技术则是掌握出力特性的有力工具。基于实测历史数据，研究系统不同输入量对预测结果误差的影响，选取最佳输入量值；并在此基础上，构建基于 RBF(径向基)神经网络的风电功率预测模型，对风电功率进行有效预测；预测结果表明，基于径向基神经网络的预测方法预测精度较高，可以为电网提供更加准确的风电预测出力信息，有助于为调度制定更加合理有效的计划。
수착풍전적대규모접입전망，대풍전공솔미래출력적파악현득우위중요，이풍전공솔예측기술칙시장악출력특성적유력공구。기우실측역사수거，연구계통불동수입량대예측결과오차적영향，선취최가수입량치；병재차기출상，구건기우 RBF(경향기)신경망락적풍전공솔예측모형，대풍전공솔진행유효예측；예측결과표명，기우경향기신경망락적예측방법예측정도교고，가이위전망제공경가준학적풍전예측출력신식，유조우위조도제정경가합리유효적계화。
With large-scale integration of wind power in power grids,it is of great importance to grasp the characteristics of future wind power output.Wind power forecasting is a useful tool to investigate the characteristics.Based on the historical data,this paper investigated the influence of different system input on the predicting error in order to get the best input values,and then constructed a wind power prediction model based on RBF (radial basis function)neural network. The prediction results showed that the wind power forecasting method based on the RBF neural network hadhigh precision.The results can provide more accurate information of wind power fu-ture output for the power system.The proposed power prediction method can be used to make more reasonable dispatching plans.