摘要
电价预测是电力市场决策的基础。文中介绍了采用小脑模型关节控制器(CMAC)神经网络建立预测提前1天不同时段的电力市场短期电价的预测模型。并以美国加州电力市场的数据作为计算实例,分别采用CMAC神经网络和反向传播算法(BP)神经网络进行短期电价预测。两种预测结果对比表明,CMAC神经网络具有所需训练样本少、输出稳定性好、计算速度快和预测精度高等优点,比较适用于短期电价预测。
Electricity price forecasting is the basis of decision making for each participant in electricity market. Using the method based on cerebellar model articulation controller (CMAC) neural network a day-ahead electricity price short-term forecasting model is established and different models are designed for different time intervals respectively. Then taking the data of California electricity market for calculation example, the short-term electricity price forcasting is performed by CMAC and BP neural network. The comparison between two results shows that using CMAC neural network the short-term electricity price can be forecasted more quickly and steadily.
出处
《电网技术》
EI
CSCD
北大核心
2003年第8期16-20,共5页
Power System Technology
关键词
短期电价预测
电力市场
小脑模型
关节控制器
神经网络
电力工业
Electricity market
Neural network
Electricity price forecasting
Cerebellar model articulation controller(CMAC)
BP
Power system
