基于门控脉冲神经P系统模型的概率负荷预测OA

Conventional deterministic load forecasting fails to provide uncertainty information of loads,while probabilistic load forecasting can generate probability distributions of predicted value uncertainty,thus providing comprehensive information for power grid dispatch decisions.In order to further improve the accuracy of probabilistic load forecasting,this paper proposes a model,which incorporates the least absolute shrinkage and selection operator(LASSO)and gated spiking neural P system(GSNP).Firstly,LASSO is used to extract the key features from external features such as minimum temperature,maximum temperature,average temperature,average humidity and precipitation.Subsequently,an improved GSNP model is developed to implement probabilistic load forecasting,enhancing the performance of long-term time-series forecasting.The case study using two long-term time-series datasets at different scales shows that the proposed model outperforms several other typical models in terms of both prediction accuracy and prediction interval quality.