电力系统中长期负荷预测综合模型研究

摘要

中长期电力负荷预测是电力系统规划的基础和重要前提.分析比较了目前中长期负荷预测方法的特点,并进行了简单的分类.对传统的电力消费弹性系数法进行了改进,结合灰色预测模型与二次滑动平均预测法,建立了新的综合预测模型,并利用遗传算法优化各单一预测模型的权重.通过与人工神经网络模型进行对比仿真验证,结果表明所提出的预测模型具有较高的预测精度和较强的适用性.%Mid-long term power load forecasting is the foundation and also an important premise of the power system planning. This paper analyzes and compares the characteristics of the present mid-long term load forecasting methods, and then classifies them simply. It improves the traditional electric consumption elasticity coefficient method, and combining with grey prediction model and secondary moving average method, establishes a new integrated model for load forecasting, and uses Genetic Algorithm(GA) to optimize the weight of each single forecasting model. Through simulation test with Artificial Neural Network(ANN) model, the results show that the proposed prediction model is of high precision and strong applicability.