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Comparison of data-driven statistical techniques for cooling demand modelling of electric chiller plants in commercial districts
Journal of Building Performance Simulation ( IF 2.5 ) Pub Date : 2021-08-18 , DOI: 10.1080/19401493.2021.1960423
Mohammad Hassan Fathollahzadeh 1 , Paulo Cesar Tabares-Velasco 1
Affiliation  

This paper models and forecasts the cooling demand of centralized chiller plants in commercial districts using different statistical techniques – including OLS, Lasso, ARX, SARIMA, SARIMAX and Cochrane–Orcutt. Direct estimation of the test error using a validation data set is used to compare different techniques and to quantify goodness of fit. As a validation, these statistical techniques are compared for forecasting the cooling demand of the largest chilled water plant on the Colorado School of Mines’ campus. Overall, Cochrane–Orcutt provides the highest accuracy among these techniques, with a lowest MSE, RMSE, CV-RMSE and MBE and highest r2 value. As a showcase of the capabilities of the developed cooling demand, the predicted demand is coupled with Hydeman et al.’s electric chiller model to predict chiller’s electric demand. The RMSE, CV-RMSE and r2 of the electric chiller model are 22.7 kWe, 17% and 0.84, respectively.



中文翻译:

商业区电冷水机组制冷需求建模数据驱动统计技术比较

本文使用不同的统计技术(包括 OLS、Lasso、ARX、SARIMA、SARIMAX 和 Cochrane-Orcutt)对商业区集中式冷水机组的制冷需求进行建模和预测。使用验证数据集直接估计测试误差用于比较不同的技术并量化拟合优度。作为验证,比较了这些统计技术来预测科罗拉多矿业学院校园内最大的冷冻水厂的冷却需求。总体而言,Cochrane-Orcutt 在这些技术中提供了最高的准确度,具有最低的 MSE、RMSE、CV-RMSE 和 MBE,最高的r 2价值。作为已开发冷却需求能力的展示,预测需求与 Hydeman 等人的电动冷水机模型相结合,以预测冷水机的电力需求。电冷水机组型号的 RMSE、CV-RMSE 和r 2分别为 22.7 kW e、17% 和 0.84。

更新日期:2021-08-18
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