Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm

The mid-term electrical energy consumption forecasting for crude oil pipelines is helpful for making important decisions, such as energy consumption target setting, unit commitment, batch scheduling, and equipment monitoring with degraded performance. The electricity energy consumption forecasting during operation is complicated. Therefore, A hybrid prediction method combining genetic algorithm and support vector machine is proposed, which includes four parts: data preprocessing part, optimization part, forecasting part, and evaluation part. The stratified sampling method is adopted to divide the training set and the test set to avoid large deviation caused by sampling stochasticity of small samples. According to the nonlinear relationship between input variable and output variable mapped by SVM technology, genetic algorithm was proposed to optimize the hyperparameters of SVM. For the operation data of three crude oil pipelines in China, the different proportions of data sets are compared and analyzed, the ratio of training set to test set for Pipeline 1, Pipeline 2, and Pipeline 3 is 6:4, 7:3, 8:2, respectively. Comparing the evaluation indicators of GA-SVM with that of five state-of-the-art prediction methods, GA-SVM hybrid model has the best effect in improving the predictive accuracy, and the forecast results are in the best agreement with the actual data. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A hybrid prediction method combining genetic algorithm and support vector machine is proposed for mid-term electrical energy consumption forecasting for crude oil pipelines, showing significant improvement in predictive accuracy. Forecasting mid-term electricity consumption can help make important decisions and enhance prediction accuracy.