基于SCA-CHHO-ELM的短期电力负荷预测

Short-term power load forecasting based on SCA-CHHO-ELM

准确的电力负荷预测是保证电网稳定运行的基础,也是电力规划的重要依据,为了提高电力负荷预测的精度,提出了一种新的预测模型,首先采用混沌策略与正余弦扰动策略对哈里斯鹰算法进行优化,然后用改进的哈里斯鹰算法对极限学习机的权值和阈值进行优化,最后用该模型进行短期电力负荷预测。对比其他预测模型可得,该模型的预测效果大大提高,并且具有更好的泛化能力与稳定性。

Accurate power load forecasting is the basis to ensure the stable operation of the power grid,and is also an important basis for power planning.In order to improve the accuracy of power load forecasting,a new forecasting model is proposed in this paper.First,the Harris Hawk algorithm is optimized by using chaos strategy and sine and cosine perturbation strategy,and then the weight and threshold of the limit learning machine are optimized by using the improved Harris Hawk algorithm.Finally,the model is used for short-term power load forecasting.Compared with other prediction models,the prediction effect of this model is greatly improved,and it has better generalization ability and stability.