摘要
短期负荷预测结果对电力系统的经济效益具有重要影响。人工鱼群算法是最新提出的新型寻优策略,具有良好的克服局部极值、获得全局极值的能力。文章建立了一种新的人工鱼群神经网络预测模型,利用人工鱼群算法训练神经网络的权值,再将该神经网络用于短期负荷预测。对某电力系统进行的负荷预测结果表明,该方法与传统的BP神经网络预测方法相比具有较强的自适应能力和较好的预测效果。
As a non-linear optimal problem short-term load forecasting impacts on economic benefit of power system greatly. Based on the individual local searching the artificial fish-swarm algorithm (AFSA) is an up-to-date proposed optimal strategy, which possesses good capability to avoid the local extremum and obtain the global extremum. Here, a new artificial neural network (ANN) based forecasting model using AFSA is built in which the weights of ANN are trained by AFSA, then the neural network using AFSA is applied to short term load forecasting. Applying the presented forecasting method to a certain actual power network it is shown that comparing with traditional BP neural network forecasting method the presented forecasting method has better adaptive ability and can give better forecasting result.
出处
《电网技术》
EI
CSCD
北大核心
2005年第11期36-39,共4页
Power System Technology
