论文标题
一种新型的MDPSO-SVR混合模型，用于电力消耗中的特征选择
A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting
论文作者
论文摘要
电力消耗预测对于一个国家的能源计划至关重要。在启用机器学习模型中，支持向量回归（SVR）已被广泛用于设置预测模型，因为它对看不见的数据的卓越概括。但是，预测建模的一个关键过程是特征选择，如果选择不正确的功能，这可能会损害预测准确性。在这方面，在本研究中采用了修改的离散粒子群优化（MDPSO）进行特征选择，然后构建了MDPSO-SVR混合模式来预测未来的电力消耗。与其他公认的同行相比，MDPSO-SVR模型在两个现实世界中的电力消耗数据集中始终如一地表现最好，这表明用于特征选择的MDPSO可以提高预测准确性，并且配备了MDPSO的SVR可以是用于电力消耗预测的有希望的替代方案。
Electricity consumption forecasting has vital importance for the energy planning of a country. Of the enabling machine learning models, support vector regression (SVR) has been widely used to set up forecasting models due to its superior generalization for unseen data. However, one key procedure for the predictive modeling is feature selection, which might hurt the prediction accuracy if improper features were selected. In this regard, a modified discrete particle swarm optimization (MDPSO) was employed for feature selection in this study, and then MDPSO-SVR hybrid mode was built to predict future electricity consumption. Compared with other well-established counterparts, MDPSO-SVR model consistently performs best in two real-world electricity consumption datasets, which indicates that MDPSO for feature selection can improve the prediction accuracy and the SVR equipped with the MDPSO can be a promised alternative for electricity consumption forecasting.
