Medium and short-term load forecasting based on NPMA-LSSVM algorithm in the case of unbalance and minority sample data

Aiming at the problem of low accuracy of load forecasting caused by unbalanced minority class samples in power load data, this paper proposes a power load forecasting method based on K-means-SyMProD-PCA data preprocessing and NPMA-LSSVM model. The improved K-means method is used to find a suitable cluster center to classify the load data, and construct a classification label as the input feature. For the sample category unbalance problem after power load classification, synthetic minority based on probabilistic distribution (SyMProD) method is used to expand the number of minority samples to balance the sample category. In order to eliminate the features with repeated information, principal component analysis (PCA) is used to extract the main features of power load. Finally, the least square support vector machine (LSSVM) load forecasting model is established, and the improved mayfly algorithm with nonlinear inertia factor and polynomial variation is used to optimize the model parameters to achieve accurate load forecasts. Using the 9th Electrician Cup Modeling Contest data set and the power consumption data set of 1443 enterprises in Yangzhong City in 2015 as the verification data, the results show that the load data processed by K-means-SyMProD-PCA input into the NPMA-LSSVM model can reduce the forecasting error effectively, it can better solve the problem of short and medium-term power load forecasting in the case of unbalanced minority class samples, which has certain applicability.