| 短期负荷预测模型及其影响因素 |
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| 引用本文： | 廖立,忻建华,翟海青,卫振华.短期负荷预测模型及其影响因素[J].上海交通大学学报,2004,38(9):1544-1547. |
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| 作者姓名： | 廖立 忻建华 翟海青 卫振华 |
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| 作者单位： | 1. 上海交通大学,机械与动力工程学院,上海,200240
2. 上海市电力公司,上海,200025 |
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| 摘 要： | 在分析电力负荷曲线特性的基础上，将日负荷分成工作日和非工作日，并着重考虑温度对负荷曲线特性的影响，将BP算法和模拟退火(SA)算法相结合，对某电网的日负荷数据进行实际计算，发现考虑预测日类型和温度等因素后，负荷预测精度有很大提高．
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| 关 键 词： | 短期负荷预测 神经网络 模拟退火 |
| 文章编号： | 1006-2467(2004)09-1544-04 |
| 修稿时间： | 2003年9月1日 |
Short Term Load Forecasting Model and the Influencing Factors |
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| LIAO Li,XIN Jian-hua,ZHAI Hai-qing,WEI Zhen-hua.Short Term Load Forecasting Model and the Influencing Factors[J].Journal of Shanghai Jiaotong University,2004,38(9):1544-1547. |
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| Authors: | LIAO Li XIN Jian-hua ZHAI Hai-qing WEI Zhen-hua |
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| Institution: | LIAO Li~1,XIN Jian-hua~1,ZHAI Hai-qing~2,WEI Zhen-hua~1 |
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| Abstract: | Based on the analysis of the load's character, the data is differentiated according to the type of day. At the same time, the influence of temperature on the load's character is considered. This paper proposed a neural network with BP & SA algorithm, which combines the property of BP with the property of SA algorithm. The numerical tests show that the accuracy will be improved after considering the influence of the day type and temperature. |
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| Keywords: | short term load forecasting neural networks simulated annealing (SA) |
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