Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network (BP-ANN) based method for short-term load forecasting is presented ...Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network (BP-ANN) based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid (China) based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.展开更多
An accurate short-term forecasting method for load of electric power system can help the electric power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis ...An accurate short-term forecasting method for load of electric power system can help the electric power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the short-term load of electric power system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical load data information of the Tai power system. The good agreements between the realistic values and forecasting values are obtained;the numerical results show that the proposed forecasting method is accurate and reliable.展开更多
针对微电网短期负荷预测精度不够的问题,论文提出了一种基于双向长短时记忆(bidirectional long short-term memory,Bi-LSTM)深度学习的负荷预测方法。将影响家庭和商业负荷分布形成的参数为输入变量,以微电网的家庭和商业总负荷分布为...针对微电网短期负荷预测精度不够的问题,论文提出了一种基于双向长短时记忆(bidirectional long short-term memory,Bi-LSTM)深度学习的负荷预测方法。将影响家庭和商业负荷分布形成的参数为输入变量,以微电网的家庭和商业总负荷分布为目标,利用输入变量对Bi-STM网络进行训练,通过识别微电网的消费模式,对微电网负荷进行时预测。利用相关系数(R)、均方误差(MSE)和均方根误差(RMSE)等性能评价指标对预测结果进行分析。结果表明,Bi-LSTM方法具有较高的相关系数。展开更多
文摘Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network (BP-ANN) based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid (China) based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.
文摘An accurate short-term forecasting method for load of electric power system can help the electric power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the short-term load of electric power system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical load data information of the Tai power system. The good agreements between the realistic values and forecasting values are obtained;the numerical results show that the proposed forecasting method is accurate and reliable.
文摘针对微电网短期负荷预测精度不够的问题,论文提出了一种基于双向长短时记忆(bidirectional long short-term memory,Bi-LSTM)深度学习的负荷预测方法。将影响家庭和商业负荷分布形成的参数为输入变量,以微电网的家庭和商业总负荷分布为目标,利用输入变量对Bi-STM网络进行训练,通过识别微电网的消费模式,对微电网负荷进行时预测。利用相关系数(R)、均方误差(MSE)和均方根误差(RMSE)等性能评价指标对预测结果进行分析。结果表明,Bi-LSTM方法具有较高的相关系数。