Adaptive Hybrid Optimized Support Vector Regression with Lasso Feature Selection for Short-term Load Forecasting.

Accurate short-term load forecasting (STLF) is of positive significance to the effective management of power companies and the stable operation of society. In spite of many studies conducted in this field, there are few to consider the inherent disadvantages of an individual module, which results in sub-optimal forecasting accuracy. Therefore, by integrating data preprocessing module and optimization module into support vector regression (SVR) forecasting module, this paper successfully presents a novel model (AHO-Lasso-SVR). The data preprocessing module, which is comprised of feature construction and Lasso feature selection, is used to construct and select meaningful features. An adaptive hybrid optimization (AHO) algorithm is proposed by introducing two strategies on the basis of standard particle swarm optimization (PSO). The AHO algorithm inevitably increases the computational complexity of model learning, thus, this paper proposes a subsampling technology to improve the optimization efficiency of the algorithm based on the sparsity of SVR. The proposed model is used to forecast the load at 48 points in the next day. To verify the properties of the proposed model, power load data from New South Wales, Australia are adopted as a case study. The results reveal that our model positively exceeds all comparison models in terms of forecasting accuracy and stability. [ABSTRACT FROM AUTHOR]