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Medium-to-Long Term City-level Electricity Consumption Forecasting Based on Cointegration-Granger Tesing and Error Correction Model

机译：基于协整格式格子测试的中长期城市级电力消耗预测及纠错模型

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摘要

With its economic society gradually entering the new normal, China's industrial structure has undergone great changes. Traditionally, predicting electricity demand only by historical data of electric energy consumption will result in the great deviation. It exists the highly coupling relationship between energy consumption and economic growth, by which the accuracy of electricity forecasting will be improved. Based on co-integration theory, Granger testing and error correction model, a novel methodology for medium-to-long term electricity forecasting is presented in this paper. Firstly, a longterm equilibrium model associated with electricity consumption and GDP is established by Granger and co-integration testing. Then, the error correction model is used to adjust the short-term fluctuation of the variables of the proposed prediction model. The analysis results in case studies verify the effectiveness of the prediction model, and demonstrate the short-term adjustment technique can improve the prediction accuracy.
机译：随着经济社会逐步进入新的正常，中国的产业结构发生了很大的变化。传统上，仅通过电能消耗的历史数据预测电力需求将导致巨大的偏差。它存在能量消耗和经济增长之间的高耦合关系，从而改善了电力预测的准确性。基于共同整合理论，GRANGER测试和纠错模型，本文提出了一种新的长期电力预测的新方法。首先，由格兰杰和共聚合测试建立了与电力消耗和GDP相关的长期均衡模型。然后，误差校正模型用于调整所提出的预测模型的变量的短期波动。案例研究的分析结果验证了预测模型的有效性，并证明了短期调节技术可以提高预测准确性。

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