Background Due to the influence of power market reform policies, the conversion of power loads has become more and more complicated. The current load forecasting methods have long calculation times and inaccurate volatility load forecasting. The difficulty of forecasting is becoming greater and more accurate. It becomes very important to predict the electrical load. Under this background, this paper proposes the application methods of collaborative knowledge mining and SMO in solving prediction

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... models based on hyperball support vector machine (CKM / SMO-SVM). Methods This study first analyzes the impact of historical data on samples and different parameters. The prediction of power load, sample data and various parameters have a significant impact on the prediction results. Secondly, applying weak entropy theory for collaborative knowledge mining, preprocessing sample data and historical information. Third, a short-term power load forecasting system based on the hypersphere support vector machine model is established and the problem is solved by SMO. Finally, the SVM model and BP model are selected for prediction to verify the new model. Results Our research proves that the rms relative error of the CKM / SMO-SVM model is only 2.32%, which is 0.67% and 1.56% lower than the SVM and BP models, respectively, and the optimization speed is faster. Conclusions The model proposed in this paper utilizes Hyper-sphere SVM which is suitable for Gaussian kernel function to achieve faster and more accurate load forecasting, which can provide more accurate services for energy spot transactions and energy scheduling plans.