利用数值天气预报进行热电联产电厂发电规划

M. Kursa, Sławomir Walkowiak, Lukasz Ligowski, W. Rudnicki
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引用次数: 0

摘要

热电联产厂的热电产量取决于天气,因此预测产量取决于天气预报。本文介绍了基于COAMPS和UM两种天气预报模型的产热模型。基于预测气温的线性模型可以解释高达90%的生产变异性,并且随着预测范围的扩大而缓慢恶化。基于UM天气预报的产热模型明显优于基于COAMPS天气预报的模型。采用机器学习算法随机森林对基本模型进行改进。为此,使用各种气象变量以及控制城市居民活动的变量(例如一天中的小时或一周中的哪一天)来预测线性模型的残差。与原始模型相比,这种机器学习方法带来了微小但显著的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilising numerical weather forecast for planning electricity production in cogeneration plant
Production of heat and electricity in the cogeneration plant depends on weather, thus forecasting production is dependent on weather forecasts. Here we present the models of the heat production based on two weather forecast models, COAMPS and UM. The linear models that are based on the predicted air temperature can explain up to 90% of variability of production and deteriorate slowly with the range of forecast. The models of heat productions that are based on UM weather forecasts significantly outperforms those that are based on the models based on the COAMPS weather forecasts. The machine learning algorithm random forest is used to improve the basic models. To this end the residuals from the linear models are predicted using various meteorological variables along with variables governing activity of city inhabitants, such as hour of the day or day of the week. This machine learning approach leads to small but significant improvement in comparison to the original model.
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