Grasping the change rule and trend of power load characteristic is the key to establish load forecasting model.
掌握电力负荷特性的变化规律和发展趋势是建立负荷预测模型的关键。
Power system load forecasting using stochastic system state model identification technique is proposed.
本文将随机系统状态模型辨识技术用于电力系统负荷预报。
Because of the existence of the indefinite factors in power load forecasting, a new load forecasting model - the fuzzy inference forecasting is presented.
考虑到电力负荷预测问题中存在的不确定性，采用模糊推理预测方法进行负荷预测的研究。
According to the trait of the power load forecasting. this paper proposes the genetic neural network load forecasting model.
根据电力负荷预测的特点，提出遗传神经网络负荷预测模型。
Application examples show that it is feasible to apply the improved PSO to the weight solution of power load combination forecasting model.
通过应用实例证明，将改进的粒子群优化算法应用到电力负荷组合预测模型的权重求解是可行的。
The variable selection, data preprocessing and model structure of power load forecasting, and their effects on forecasting precision are discussed.
着重论述了电力负荷预测中建模变量的选择、数据的预处理方法、模型的拓扑结构及其对预测精度的影响。
In this paper, the method of weighted least square estimate is proposed to construct ARMA model, which can be applied in power system load forecasting.
采用加权最小二乘法参数估计方法，得到应用于电力系统日负荷预测和月负荷预测的ARMA模型。
Currently, there are a lot of models for power load forecasting. Only one model cannot totally reflect the changing rules and information of power load.
目前，负荷预测的模型很多，单一的一种模型不能完全反映电力负荷的变化规律和信息。
The short-term load forecast model was established based on the AVCPSO-RBF algorithm. Using the method and history load data of Guizhou power system, the short-term load forecasting was carried out.
建立了基于该优化算法的短期负荷预测模型，利用贵州电网历史数据进行短期负荷预测。
The short-term load forecast model was established based on the AVCPSO-RBF algorithm. Using the method and history load data of Guizhou power system, the short-term load forecasting was carried out.
建立了基于该优化算法的短期负荷预测模型，利用贵州电网历史数据进行短期负荷预测。
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