Thermal models of buildings are helpful to forecast their energy use and to enhance the control of their mechanical systems.However,these models are building-specific and require a tedious,error-prone and time-consumi...Thermal models of buildings are helpful to forecast their energy use and to enhance the control of their mechanical systems.However,these models are building-specific and require a tedious,error-prone and time-consuming development effort relying on skilled building energy modelers.Compared to white-box and gray-box models,data-driven(black-box)models require less development time and a minimal amount of information about the building characteristics.In this paper,autoregressive neural network models are compared to gray-box and black-box linear models to simulate indoor temperatures.These models are trained,validated and compared to actual experimental data obtained for an existing commercial building in Montreal(QC,Canada)equipped with roof top units for air conditioning.Results show that neural networks mimic more accurately the thermal behavior of the building when limited information is available,compared to gray-box and black-box linear models.The gray-box model does not perform adequately due to its under-parameterized nature,while the linear models cannot capture non-linear phenomena such as radiative heat transfer and occupancy.Therefore,the neural network models outperform the alternative models in the presented application,reaching a coefficient of determination R2 up to 0.824 and a root mean square error down to 1.11℃,including the error propagation over time for a 1-week period with a 5-minute time-step.When considering a 50-hour time horizon,the best neural networks reach a much lower root mean square error of around 0.6℃,which is suitable for applications such as model predictive control.展开更多
Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes. The fuzzy C-means algorithm was evaluated f...Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes. The fuzzy C-means algorithm was evaluated for rule base generation for fuzzy and fuzzy grey-box temperature estimation. Experimental data were collected from a real- life mill and three different sets were randomly drawn. The first set was used for rule-generation, the second set was used for training those systems with learning capabilities, while the third one was used for validation. The perform- ance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant. The results show that the fuzzy C-means generated rule-bases improve temperature estimation; however, the best results are obtained when fuzzy C-means algorithm, grey-box modeling and learning functions are combined. Application of fuzzy C-means rule generation brings improvement on performance of up to 72%.展开更多
基金The research work presented in this paper is financially supported by the Institute for Data Valorization(IVADO).
文摘Thermal models of buildings are helpful to forecast their energy use and to enhance the control of their mechanical systems.However,these models are building-specific and require a tedious,error-prone and time-consuming development effort relying on skilled building energy modelers.Compared to white-box and gray-box models,data-driven(black-box)models require less development time and a minimal amount of information about the building characteristics.In this paper,autoregressive neural network models are compared to gray-box and black-box linear models to simulate indoor temperatures.These models are trained,validated and compared to actual experimental data obtained for an existing commercial building in Montreal(QC,Canada)equipped with roof top units for air conditioning.Results show that neural networks mimic more accurately the thermal behavior of the building when limited information is available,compared to gray-box and black-box linear models.The gray-box model does not perform adequately due to its under-parameterized nature,while the linear models cannot capture non-linear phenomena such as radiative heat transfer and occupancy.Therefore,the neural network models outperform the alternative models in the presented application,reaching a coefficient of determination R2 up to 0.824 and a root mean square error down to 1.11℃,including the error propagation over time for a 1-week period with a 5-minute time-step.When considering a 50-hour time horizon,the best neural networks reach a much lower root mean square error of around 0.6℃,which is suitable for applications such as model predictive control.
文摘Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes. The fuzzy C-means algorithm was evaluated for rule base generation for fuzzy and fuzzy grey-box temperature estimation. Experimental data were collected from a real- life mill and three different sets were randomly drawn. The first set was used for rule-generation, the second set was used for training those systems with learning capabilities, while the third one was used for validation. The perform- ance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant. The results show that the fuzzy C-means generated rule-bases improve temperature estimation; however, the best results are obtained when fuzzy C-means algorithm, grey-box modeling and learning functions are combined. Application of fuzzy C-means rule generation brings improvement on performance of up to 72%.
