基于微源控制-小波神经网络的微网功率预测

针对可再生能源发电具有功率周期变化与对环境敏感的双重性，提出将微源控制(MSC)用入分布式电网功率预测的小波神经网络模型学习算法。该算法在灵活处理功率局部与周期特性的基础上，结合环境因素对功率变化的影响，引入关联因子优化权重，得出最终预测结果。通过对实际微网系统的仿真测试，并与BP神经网络与GRNN模型进行比较，研究结果表明：MSC-WNN模型在三次测试中相对误差均在-1%-1%以内，说明了其具有较高预测精度和良好的鲁棒性能。

Micro-grid Power Forecast Based on MSC-WNN Model

Considering the duality of cyclical changes and environmentally sensitive in micro-grid power output, micro source control (MSC)is used into the learning algorithm of wavelet neural network model to predict distributed power grid power. Based on the flexibility of dealing with the local and cycle nature of the power, and combined with the impact of environmental factors on the power change, the correlation factor is introduced into the algorithm to optimize the weight and gets the final prediction results. Through the actual network system simulation test, and compared with BP neural network and GRNN model, the results show that: the relative errors of MSC-WNN model in three tests were within -1% to 1%, showing its high precision and good robust performance.