摘要
神经网络具有万能逼近能力,在模式识别、模型预测和数据挖掘等领域得到了广泛应用。但是,神经网络在被逼近非线性函数峰值处的误差较大,当峰值两侧的斜率差较大时误差更大。提出了基于粗糙集理论的改进神经网络算法,并将其应用于短期负荷预测。将当前时间间隔负荷、前一时间间隔负荷、当前时间间隔和前一时间间隔的负荷差和当前时间分别作为神经网络预测模型的输入,将下一时间间隔的预测负荷作为神经网络的输出,利用粗糙集理论对神经网络预测模型输出的预测负荷进行补偿,使预测精度更高。仿真结果表明,该方法能显著提高函数的预测精度。
Due to its remarkable approximation ability, neural network is widely applied in pattern recognition, model prediction and data mining. However, the approximation error of neural network at the peak value of the approximated nonlinear function is great, especially the error is greater when the slope difference at both sides of the peak value. An improved rough set-based neural network algorithm is proposed and applied in short-term load forecasting. Taking the load in current time interval, load in prior time interval, load difference between current time interval and prior time interval and current time as the inputs of neural network forecasting model, and the forecasted load in next time interval as the output of neural network forecasting model, the forecasted load, i.e., the output of neural network forecasting model, is compensated according to rough set theory to improve the forecasted result. Simulation results show that using the proposed method the precision of load forecasting can be evidently improved.
出处
《电网技术》
EI
CSCD
北大核心
2010年第12期168-173,共6页
Power System Technology
基金
国家自然科学基金资助项目(50777040)
中国博士后科学基金资助项目(20090461204)
山东省博士后创新项目专项资金资助项目(200903066)
山东省高等学校科技计划项目(J09LG09)。~~
关键词
负荷预测
神经网络
粗糙集理论
load forecasting： neural network： rough set theory
