Low Order Grey-box Models for Short-term Thermal Behavior Prediction in Buildings

Fonti, Alessandro; Comodi, Gabriele; Pizzuti, Stefano; Arteconi, Alessia; Helsen, Lieve

doi:10.1016/j.egypro.2017.03.592

Cited by 27 publications

(13 citation statements)

References 5 publications

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“…Results show that this dataset explains the best the building's dynamics since all models' orders present satisfactory performances with the greatest fit percentage and the lowest RMSE. This confirms the results obtained in [15], where the order 2 was the most performing order with RMSE less than 0.5 • C and fit percent equal to 94% for 1-hour prediction. Figure 7 shows the error distribution corresponding to the second order.…”

Section: Experiments C (Heating Power = 1500 W)supporting

confidence: 90%

“…Grey box models combine building physics and statistics. Physical knowledge derived from the buildings' dynamics is formulated by stochastic differential equations in the state space form [15]. Statistical measurements present information embedded in the collected data.…”

Section: Grey Box Modelingmentioning

confidence: 99%

“…The model's parameters were determined using 'Greyest' function in 'Matlab'. This function provides the maximum likelihood using the following algorithms: The Gauss-Newton direction, the Levenberg-Marquardt and the steepest descent gradient [15,18].…”

Section: Parameters Estimationmentioning

confidence: 99%

“…Hedgaard and Peterson [13] showed that the second and third-order models produce a good short-time forecasting of the building's thermal behavior. Fonti et al [15] used experimental studies to analyze the accuracy of grey-box models for the short-term prediction of a building. Results showed that the second-order models provide the required accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Determination of the Optimal Order of Grey-Box Models for Short-Time Prediction of Buildings’ Thermal Behavior

et al. 2019

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The use of grey-box models for short-time forecasting of buildings' thermal behavior requires the determination of the models' order since this order could influence the grey-box models' performance. This paper presents an analysis of the optimal order of these models for different thermal conditions. The novelty of this work consists of considering the influence of the heating conditions on the determination of the performances of grey-box models. The analysis is based on experimental tests that were conducted in a room with different thermal conditions, related to the variation of the heating power. Experimental results were used for the determination of the optimal grey-box models' order that minimizes the gap between the experimental results and the grey-box forecasting. Results show that the optimal grey-box models' order depends on the buildings' thermal conditions, but generally lies between two and three with an error less than 0.2 • C and a fit percent greater than 90%.

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Section: Experiments C (Heating Power = 1500 W)supporting

confidence: 90%

Section: Grey Box Modelingmentioning

confidence: 99%

Section: Parameters Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determination of the Optimal Order of Grey-Box Models for Short-Time Prediction of Buildings’ Thermal Behavior

et al. 2019

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“…Zhao et al [12] developed a data-mining model that learns the behavior of occupants based on the office appliance usage. Grey-box models have also been predominantly used in studying short-term behavior of buildings [13].…”

Section: Energy Prediction In Buildingsmentioning

confidence: 99%

Development of Regression Models considering Time‐Lag and Aerosols for Predicting Heating Loads in Buildings

Lim

Kim

2018

Advances in Civil Engineering

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Building automation systems is becoming more vital, especially in regard to reduced building energy consumption. However, the accuracy of such systems in calculating building thermal loads is limited as they are unable to predict future thermal loads based on prevailing environmental factors. e current paper therefore seeks to improve the understanding of the interactions between outdoor meteorological data and building energy consumption through a statistical analysis. Using weather data collected by the Korean Meteorological Agency (KMA) over a period of three years (2011-2014), prediction models that are able to predict heating thermal loads considering the time-lag phenomenon are developed. In addition, the study develops different prediction models for buildings of different sizes. e results confirm the existence of the time-lag phenomenon: the heating load experienced by a building at a given time is better explained by a regression model developed using the climatic conditions that existed two hours before. As such, conventional building simulation programs must endeavor to include time-lag as well as Aerosol Optical Depth (AOD) data as important factors in the prediction of building heating loads.

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System identification using NARX and centrifugal compressor control through the intelligent, active method—Case study: K‐250 centrifugal compressor

Khosravi

Chatraei

Shahgholian

et al. 2022

Asian Journal of Control

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Surge and constant pressure are some of the most critical issues in compressor control. In this paper, the problem of the surge and constant pressure in the presence of environmental disturbances is solved. Proposed design for control system based on proportional-integral controllers, adaptive neuro-fuzzy inference system (FIS), and particle swarm optimized neural fuzzy and for modeling neural network strategy fuzzy nonlinear automatic regression with external input is used. Based on this, for modeling, practical and real data are extracted from the K-250 compressor of Isfahan Steel Company. In the adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO)

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Low Order Grey-box Models for Short-term Thermal Behavior Prediction in Buildings

Cited by 27 publications

References 5 publications

Determination of the Optimal Order of Grey-Box Models for Short-Time Prediction of Buildings’ Thermal Behavior

Determination of the Optimal Order of Grey-Box Models for Short-Time Prediction of Buildings’ Thermal Behavior

Development of Regression Models considering Time‐Lag and Aerosols for Predicting Heating Loads in Buildings

System identification using NARX and centrifugal compressor control through the intelligent, active method—Case study: K‐250 centrifugal compressor

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