| 引用本文: | 胡尧,李红莲,王赏玉,杨柳.辐射数据缺失时TMY与逐时值生成方法分析[J].哈尔滨工业大学学报,2022,54(6):163.DOI:10.11918/202012079 |
| HU Yao,LI Honglian,WANG Shangyu,YANG Liu.Analysis of typical meteorological year and hourly value generation method with radiation data missing[J].Journal of Harbin Institute of Technology,2022,54(6):163.DOI:10.11918/202012079 |
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| 摘要: |
| 为解决太阳辐射数据缺失导致无法准确地进行建筑能耗模拟、建筑节能等相关分析,以西安市为例,研究太阳辐射数据缺失时典型气象年（TMY）挑选与逐时辐射预测。首先,通过相关性分析得出容易获取的日照时数与太阳辐射相关性最高,因此在传统Sandia方法的基础上采用日照时数替代辐射的方法挑选TMY,并对挑选结果进行参数对比分析,验证了新参数挑选出TMY的准确性。其次,通过主要太阳辐射影响参数对比,选择合理的预测输入参数,选用处理泛化问题能力较强的神经网络及其优化算法进行逐时辐射预测研究,并将得到的结果与统计模型和观测数据进行对比分析。最后,参照中国建筑节能设计标准建立办公建筑模型,并利用本研究提出的方法得出的气象数据进行建筑能耗模拟验证,分别分析了建筑采暖与制冷能耗的变化情况。结果表明,提出的TMY挑选方法可以很好地解决辐射数据缺失地区挑选TMY的难题,神经网络算法可以对逐时辐射数据进行较精准的预测,为辐射数据缺失研究建筑节能提供新思路。 |
| 关键词: 太阳辐射 日照时数 典型气象年 神经网络 能耗模拟 |
| DOI：10.11918/202012079 |
| 分类号:TU14 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2018YFC0704504) |
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| Analysis of typical meteorological year and hourly value generation method with radiation data missing |
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HU Yao1,LI Honglian1,2,WANG Shangyu2,YANG Liu2
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(1.College of Information and Control Engineering, Xian University of Architecture and Technology, Xian 710055, China; 2.State Key Laboratory of Green Building in Western China (Xian University of Architecture and Technology), Xian 710055, China)
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| Abstract: |
| In view of the problem that the lack of solar radiation data leads to the inaccuracy of building energy consumption simulation and building energy conservation analysis, this paper takes Xian as an example to study the selection of typical meteorological year (TMY) and prediction of hourly radiation when the solar radiation data is missing. First, through correlation analysis, it was found that the easily obtained sunshine duration had the highest correlation with solar radiation. Therefore, on the basis of the traditional Sandia method, TMY was selected according to sunshine duration instead of radiation, and the parameters of the selection results were compared and analyzed to verify the accuracy of TMY selected by the new parameters. Then, reasonable prediction input parameters were selected through the comparison of the main influencing parameters of solar radiation, and hourly radiation prediction was carried out by using neural network and its optimization algorithm, which has strong ability in dealing with generalization problems. The obtained results were compared with statistical model and observation data. Finally, an office building model was established according to the building energy conservation design standards in China. The meteorological data obtained by the proposed method were used to simulate and verify the energy consumption of the building, and the changes in the heating and cooling energy consumption were analyzed respectively. Results show that the proposed TMY selection method could well solve the problem of selecting TMY in areas with radiation data missing, and the neural network algorithm could accurately predict hourly radiation data, which provides a new idea for the study of building energy conservation with radiation data missing. |
| Key words: solar radiation sunshine duration typical meteorological year neural network energy consumption simulation |
