| 基于H-P滤波预测技术的年用电量预测模型研究 |
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| 引用本文： | 曾 鸣,陈春武,刘 洋,马明娟,钱 霞.基于H-P滤波预测技术的年用电量预测模型研究[J].水电能源科学,2012,30(8):175-178. |
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| 作者姓名： | 曾 鸣 陈春武 刘 洋 马明娟 钱 霞 |
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| 作者单位： | 1. 华北电力大学经济与管理学院,北京,102206
2. 华电招标有限公司,北京,100031 |
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| 基金项目： | 美国能源基金会支持项目（G〖KG*9〗100612630） |
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| 摘 要： | 针对电力市场预测电力负荷受众多因素影响及各类预测模型模拟预测误差较大的问题,为提高负荷预测精度,基于H-P滤波预测法将等维信息法、指数回归模型及分布滞后回归模型引入年用电量预测中,通过双层预测降低预测误差,并结合实例比较。对比结果,滤波滞后回归模型的预测综合得分高于滤波指数回归模型。
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| 关 键 词： | 年用电量预测 H-P滤波预测法 指数回归模型 分布滞后回归模型 |
Research on Annual Electricity Consumption Forecasting Model Based on H-P Filter Forecasting Technology |
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| ZENG Ming,CHEN Chunwu,LIU Yang,MA Mingjuan and QIAN Xia.Research on Annual Electricity Consumption Forecasting Model Based on H-P Filter Forecasting Technology[J].International Journal Hydroelectric Energy,2012,30(8):175-178. |
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| Authors: | ZENG Ming CHEN Chunwu LIU Yang MA Mingjuan and QIAN Xia |
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| Affiliation: | School of Economic and Management, North China Electric Power University, Beijing 102206, China;School of Economic and Management, North China Electric Power University, Beijing 102206, China;School of Economic and Management, North China Electric Power University, Beijing 102206, China;School of Economic and Management, North China Electric Power University, Beijing 102206, China;China Huadian Tendering Company Limited, Beijing 100031, China |
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| Abstract: | Power load forecasting is affected by many factors and the prediction errors of various forecasting models are large in electricity market. For improving the accuracy of load forecasting, this paper integrates recurrence of new information with equal dimension, exponential regression model and distributed lag regression model into annual electricity consumption forecast based on the H P filter prediction method. And then the double layer prediction is adopted to reduce prediction error. Comparative analysis of examples, the results show that the prediction score of filter lag regression model is higher than that of filter exponential regression model. |
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| Keywords: | annual electricity consumption prediction H P filter forecasting model exponential regression model distributed lag regression model |
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