基于特征选择和iJaya-SVR的年度电力消费预测研究

Prediction of annual electricity consumption based on feature selection and iJaya-SVR

准确的电力消费预测对能源规划和政策制定具有重要意义.鉴于已有研究忽略了特征冗余以及智能优化算法控制参数不确定对预测精度的影响,引入最大相关最小冗余(MRMR)算法筛选电力消费的关键影响因素作为预测指标,提出改进的Jaya算法(iJaya)用于优化支持向量回归(SVR)的超参数,进而构建MRMR-iJaya-SVR预测模型.以我国的年度电力消费数据为例,对MRMR-iJaya-SVR模型的预测效果进行验证,并利用北京市的年度电力消费数据测试其鲁棒性.结果表明:iJaya算法具有较强的全局搜索能力和较好的稳定性, MRMR-iJayaSVR模型在单步预测和多步预测中的表现均优于基准模型.此外,对于不同的数据集, MRMR-iJaya-SVR模型均具有良好的鲁棒性.

Accurate forecasting of annual electricity consumption is of great significance for energy planning and policy making.Given that the literature ignores the impact of feature redundancy and uncertainty of algorithm-specific control parameters of an intelligent optimization algorithm on forecasting accuracy,this paper introduces a max-relevance and min-redundancy(MRMR)algorithm to select the key influencing factors as predictors,proposes an improved Jaya algorithm(iJaya)to optimize the hyper-parameters of support vector regression(SVR)and constructs the annual electricity consumption forecasting model MRMR-iJaya-SVR.Taking the real electricity consumption data of China as an example,this paper validates the forecasting performance of the MRMR-iJaya-SVR.Besides,the yearly electricity consumption data of Beijing are used to test the robustness of the proposed model.The experimental results show that the iJaya algorithm has better global searching ability and is more stable.And the proposed model outperforms benchmark models in both single-step-ahead and multi-step-ahead forecasting.Furthermore,for different datasets,the proposed model has strong robustness.