【摘要】 针对现有短期负荷预测方法适应性不足、预测精度不高,WNN原始连接权值和阈值采取随机赋值并采用梯度学习算法进行修正,存在进化缓慢、易出现陷入局部极小或不收敛等问题,提出了基于高斯FIG和改进WNN的短期负荷区间预测新方法。用收敛速度更快的函数取代常用的输出层神经元函数,并用粒子群算法寻优取代WNN连接权值和阈值随机赋值。把网络连接权值和阈值作为粒子群算法微粒的位置向量,不断调整微粒的速度和位置向量以寻求最优值。选择了合适的数据跨度作为一个粒化窗口,对原始负荷数据进行了高斯模糊粒化处理,得到了对应的高斯FIG后的序列值,并用改进后的WNN对模糊序列值进行了区间预测。与WNN及SVM方法的对比研究结果表明,该方法不仅能够获得比单一负荷值更多的区间信息,而且预测精度更高,能够更好地指导电力系统相关决策。更多还原

【Abstract】 Aiming at the problem of being lack of adaptability and forecasting accuracy of the current short-term load forecasting methods,and the problem of slow evolution,easy to fall into a local minimum and no convergence because of the random assignment of the original connection weights and threshold and adopting gradient learning algorithm to improve the correction in original WNN method,a new method based on gaussian fuzzy information granulation and improved wavelet neural network was proposed. A faster convergence speed function was proposed to replace the commonly used output layer neuron function and particle swarm algorithm optimization was used to replace the random assignment of the original connection weights and threshold of WNN. The network connection weights and threshold was taken as a particle position vector of particle swarm optimization,the speed of the particles and the position vector was adjusted constantly to find the optimal value. Appropriate data span was selected as a graining window to dispose the original load data by using gaussian fuzzy information granulation method and the corresponding sequence values after gaussian FIG were gotten. Then the improved WNN was used to do interval forecasting of the fuzzy sequence values. The results through comparative study of WNN and SVM indicate that the method proposed can not only gain range information,which is more than single load value,but also achieves higher prediction accuracy,which can better guide the power system related decision-making.更多还原