计及温度累积效应的智能电网负荷预测算法

Load forecasting algorithm of smart grid considering temperature cumulative effect

针对温度累积效应对负荷变化造成的影响,提出了一种计及温度累积效应的智能电网负荷预测算法。将持续高温对电网负荷的影响计入预测模型中,并利用模块化神经网络保证了对温度累积效应学习的独立性和准确性。由三个子网络构成多模块神经网络的第一层,以温度、时间及负荷特征为输入参数,所得负荷预测的准确度可达98.13%,且误差较修正前降低了28.63%。结果表明,所提算法具有更高的预测准确性和运行效率。

Aiming at the influence of temperature cumulative effect on load change,a load forecasting algorithm for smart grid considering temperature cumulative effect was studied.The effect of continuous high temperature on grid load was included in the forecasting model.A modular neural network was used to ensure the independence of temperature cumulative effect learning and the improvement of accuracy.Three sub-networks formed the first layer of the multi-module neural network,with temperature,time and load characteristics as input parameters.The accuracy of load forecasting is 98.13%,and the error is 28.63% lower than that before correction.The results show that the as-proposed algorithm has higher forecasting accuracy and operation efficiency.