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作 者:刘洁 柳伊 邹大伟 Jie Liu;Yi Liu;Dawei Zou(Key Laboratory of Meteorological Disaster Prevention and Mitigation of Shandong,Jinan Shandong;Tai’an Meteorological Bureau of Shandong,Tai’an Shandong)
机构地区:[1]山东省气象防灾减灾重点实验室,山东济南 [2]山东省泰安市气象局,山东泰安
出 处:《气候变化研究快报》2024年第6期1682-1687,共6页Climate Change Research Letters
基 金:山东省气象局引导类项目(2022SDYD19);山东省气象局青年项目(2021SDQN11)。
摘 要:节能是推进碳达峰、碳中和、促进高质量发展的重要支撑,供热节能是全球面临的重要难题,实现及时准确的热负荷预测是实现供热系统节能的基础。在影响热网负荷发生变化的众多因素当中,室外温度、太阳辐射、风速和相对湿度等气象因素的影响最为显著。文章阐述了近年来我国精细化气象预报技术的发展历程,回顾了气象要素与供暖热负荷相关性研究进展。我国多元化、网格化、精细化的气象预报发展,为按需供热及供热精细化调控提供了有利条件。同时随着研究手段的进步,回归分析和机器学习等基于数据支撑下的负荷预测方法被广泛应用,且在负荷预测中表现出了较高的精度,为多气象参数的负荷预测奠定了良好的基础。研究分析认为随着我国综合气象观测系统、多源实况融合分析技术和多尺度数值模式的快速发展,能够建立基于综合气象参数的气候补偿调控模型,有利于提高按需供热精细化调控水平。Energy conservation is an important support for promoting carbon peaking, carbon neutrality and high-quality development. Energy saving in a heating system is a serious challenge faced by the world. Timely and accurate heat load prediction is the basis of energy saving in heating system. Among the many factors that affect the change of heat network load, the meteorological factors such as outdoor temperature, solar radiation, wind speed and relative humidity are the most significant effects. This paper introduces the developing process of fine meteorological forecast in recent years in China, reviews the research progress of correlation between meteorological elements and heating load. Meanwhile, with the progress of research methods, load forecasting methods based on data such as regression analysis and machine learning have been widely applied, and have shown high accuracy in load forecasting, laying a good foundation for load forecasting with multiple meteorological parameters. The research speculates that, with the rapid improvement of the fine Meteorological Forecast in China, comprehensive meteorological observation system, multi-source real-time fusion analysis technique and multi-scale numerical model, a climate compensation regulation model is expected to be established based on comprehensive meteorological parameters, which is conducive to improving the level of refined regulation of on-demand heating.
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