基于BP神经网络的风电功率预澳

风具有易变性、随机性等特点，风电并网之后，可能引起运行和可靠性的问题。如果能够提前对风电功率进行准确的预测，则有利于及时调整调度计划，保证电能质量，减少系统备用容量，获得更多的经济效益和社会效益。本文介绍了这一领域的研究现状，采用了BP人工神经网络的方法，利用风速的历史数据，建立模型进行风电功率预测，仿真结果表明该方案的有效性。

Wind Power Forecast Based on BP Neural Network

Operational and reliability problems may arise after wind power grid integration, due to wind power＇s characteristics such as volatility and randomness. If wind power can be accurately forecast in advance, scheduling plan can be adjusted timely to ensure power quality, reduce system reserve capacity and acquire more economic and social benefits. This paper describes the research status in this area and builds a model for wind power prediction based on BP artificial neural network and historical wind speed data. The simulation results demonstrate the effectiveness of the model.