Short term load forecasting of urban gas based on L1-SSA-SVM model
Accurate and timely gas load prediction is crucial for fully allocating gas resources and ensuring residential gas consumption.However,gas loads themselves have non-linear characteristics,making it very difficult to establish a fixed simulation mechanism.In order to find a more accurate gas load prediction model,a gas load prediction model using L1 norm feature selection analysis correlation and support vector machine(SVM)optimization based on the Sparrow Optimization Algorithm(ISSA)is proposed.Using L1 norm feature selection to select among the 11 influencing factors related to gas load,by analyzing the correlation degree between different influencing factors,reducing the coefficients of some influencing factors,a sparse weight matrix is generated to eliminate the relatively small correlation factors,and the high correlation factors are used as inputs for SVM(Support Vector Machines,SVM).Then,ISSA(Improving Sparrow Search Algorithm,ISSA)is used to optimize the penalty factor c and kernel function parameter g of the support vector machine model,establish the ISSA-SVM model to predict urban gas load,and verify its accuracy and effectiveness.The results show that the proposed model has a MAPE of 0.65％,which is much lower than traditional support vector machine models and support vector machine models optimized by traditional sparrow search algorithms.
gas load predictionsparrow search algorithmL1 norm feature selectionsupport vector machine model
万方数据
维普
