基于数值天气预报的母线负荷综合预测模型
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摘要
母线负荷预测是电网安全校核和编制发电调度计划的基础。基于母线负荷历史数据和数值气象数据,通过相关性分析将母线负荷进行分类,提出了一种基于数值天气预报的BP神经网络母线负荷预测方法。进而以倍比平滑法和基于数值气象数据的BP神经网络法为基础,建立了分时段变权重的综合预测模型。对烟台地区的母线负荷预测结果表明,所提出的母线负荷综合预测模型有效地提高了预测准确率,对含大规模风电接入电网的输电设备安全校核具有重要的实用价值。
Bus load forecasting is the basis for power grid security checking and for power generation dispatch plan. Based on the historical data of the bus load as well as numerical weather data, bus load was classified through correlation analysis. A BP neural network bus load prediction method was proposed on the basis of numerical weather prediction. Furthermore, a composite prediction model with time-variable weight was set up in the multiple proportions smoothing method as well as the BP neural network approach based on numerical weather data. The results of prediction of bus load in Yantai area indicated that the proposed composite bus load prediction model could greatly improve prediction accuracy and have great practical value in security check for the transmission equipment in the power grid containing large-scale wind power integration.
Bus load forecasting is the basis for power grid security checking and for power generation dispatch plan. Based on the historical data of the bus load as well as numerical weather data, bus load was classified through correlation analysis. A BP neural network bus load prediction method was proposed on the basis of numerical weather prediction. Furthermore, a composite prediction model with time-variable weight was set up in the multiple proportions smoothing method as well as the BP neural network approach based on numerical weather data. The results of prediction of bus load in Yantai area indicated that the proposed composite bus load prediction model could greatly improve prediction accuracy and have great practical value in security check for the transmission equipment in the power grid containing large-scale wind power integration.
引文
[1]贺辉.电力负荷预测和负荷管理[M].北京:中国电力出版社,2013.
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[7]韩力群.人工神经网络理论、设计及应用[M].北京:化学工业出版社,2004.
[8]康重庆,夏清,沈瑜,等.电力系统负荷预测的综合模型[J].清华大学学报(自然科学版),1999,39(1):8-11.
[9]程旭,康重庆,夏清,等.短期负荷预测的综合模型[J].电力系统自动化,2000,15(6):42-44.
[10]顾洁.电力系统中长期负荷的可变权综合预测模型[J].电力系统及其自动化学报,2003,15(6):56-60.
[2]辛丽虹.电力系统母线负荷预测研究[M].成都:四川大学出版社,2000.
[3]庄楚强,吴亚森.应用数理统计基础[M].广州:华南理工大学出版社,1992.
[4]康重庆,程旭,夏清,等.一种规范化的处理相关因素的短期负荷预测新策略[J].电力系统自动化,1999,23(18):32-35.
[5]苏胜新.电力系统若干关键技术研究及应用[M].武汉:华中科技大学出版社,2005.
[6]游仕洪,程浩忠,谢宏.模糊组合预测在中长期负荷预测中的应用[J].电力系统及其自动化学报,2004,16(3):53-56.
[7]韩力群.人工神经网络理论、设计及应用[M].北京:化学工业出版社,2004.
[8]康重庆,夏清,沈瑜,等.电力系统负荷预测的综合模型[J].清华大学学报(自然科学版),1999,39(1):8-11.
[9]程旭,康重庆,夏清,等.短期负荷预测的综合模型[J].电力系统自动化,2000,15(6):42-44.
[10]顾洁.电力系统中长期负荷的可变权综合预测模型[J].电力系统及其自动化学报,2003,15(6):56-60.