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A Corrected Hybrid Approach for Electricity Demand Forecasting

机译:修正的混合电力需求预测方法

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For proper and efficient evaluation of electricity demand forecasting, a hybrid Seasonal Auto-Regression Integrated Moving Average and Least Square Support Vector Machine (SARIMA-LSSVM) model is significantly developed to forecast the electricity demand in New South Wales of Australia. The design concept of combining the Seasonal Auto-Regression Integrated Moving Average (SARIMA) method with the Least Square Support Vector Machine (LSSVM) algorithm shows more powerful forecasting capacity for daily electricity demand forecasting at electricity parks, when compared with the single SARIMA and LSSVM models. To verify the developed approach, the daily data from New South Wales of Australia is used for model construction and model testing. The simulation and hypothesis test results show that the developed method is simple and quite efficient.
机译:为了正确,有效地评估电力需求预测,开发了混合的季节自回归综合移动平均和最小二乘支持向量机(SARIMA-LSSVM)模型来预测澳大利亚新南威尔士州的电力需求。与单一的SARIMA和LSSVM相比,季节性自回归综合移动平均(SARIMA)方法与最小二乘支持向量机(LSSVM)算法相结合的设计概念显示出了更强大的预测能力,可用于每天对电力园区进行电力需求预测楷模。为了验证开发的方法,将澳大利亚新南威尔士州的每日数据用于模型构建和模型测试。仿真和假设测试结果表明,该方法简单有效。

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