Inverse blackbox modeling of the heating and cooling load in office buildings

Gunay, H. Burak; Shen, Weiming; Newsham, Guy R.

doi:10.1016/j.enbuild.2017.02.064

Cited by 70 publications

(26 citation statements)

References 27 publications

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“…To evaluate the quality of the PSO-XGBoost, XGBoost, SVM, RF, GP, and CART models, five indices of performance were used, including mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R 2 ). The calculation of RMSE, R 2 , MAE, VAF, and MAPE was described in Equations (13)- (17):…”

Section: Performance Evaluation Indicesmentioning

confidence: 99%

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

et al. 2019

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In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R2), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R2, VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities.

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Section: Performance Evaluation Indicesmentioning

confidence: 99%

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

et al. 2019

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“…Grey-box models have showed good performance and cost-benefit ratio [63], [64], even with relatively simple formulations and few input variables [65]. This kind of models rely on the existing corpus of expert knowledge to model thermal behaviour by using differential equations encoding the physical principles of mass, energy and momentum transfer; and they apply statistical models to tune model outputs based on historical and live data.…”

Section: B Simulation Model and Calibrationmentioning

confidence: 99%

A Probabilistic Algorithm for Predictive Control With Full-Complexity Models in Non-Residential Buildings

et al. 2019

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Despite the increasing capabilities of information technologies for data acquisition and processing, building energy management systems still require manual configuration and supervision to achieve optimal performance. Model predictive control (MPC) aims to leverage equipment control-particularly heating, ventilation, and air conditioning (HVAC)-by using a model of the building to capture its dynamic characteristics and to predict its response to alternative control scenarios. Usually, MPC approaches are based on simplified linear models, which support faster computation but also present some limitations regarding interpretability, solution diversification, and longer-term optimization. In this paper, we propose a novel MPC algorithm that uses a full-complexity grey-box simulation model to optimize HVAC operation in non-residential buildings. Our system generates hundreds of candidate operation plans, typically for the next day, and evaluates them in terms of consumption and comfort by means of a parallel simulator configured according to the expected building conditions (weather and occupancy). The system has been implemented and tested in an office building in Helsinki, both in a simulated environment and in the real building, yielding energy savings around 35% during the intermediate winter season and 20% in the whole winter season with respect to the current operation of the heating equipment. INDEX TERMS Model predictive control, simulation, control, building energy management system.

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“…In addition to data efficiency, we are interested in developing mathematically simple models that can be easily adopted in a wide range of power system applications. Thus, unlike the artificial neural network models in [22], [24] that employ nonlinear activation functions, our proposed model employs exclusively linear equalities and inequalities that can be easily embedded in most power system optimization and control settings. The work in [23] is closely related to ours since it also identifies a coarse (e.g., facility-level) models of thermal dynamics.…”

Section: B Related Workmentioning

confidence: 99%

“…The mathematical simplicity of our model stands in contrast to neural network-based models like the ones in [22], [24]. While such models are useful for certain applications, e.g., local load control, their non-linear representations makes them ill-suited for UC and market models.…”

Section: A Tractable and Robust Approximation Of The Feasible Rementioning

confidence: 99%

Tractable and Robust Modeling of Building Flexibility Using Coarse Data

Contreras-Ocaña

Ortega‐Vazquez

Kirschen

et al. 2018

IEEE Trans. Power Syst.

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Controllable building loads have the potential to increase the flexibility of power systems. A key step in developing effective and attainable load control policies is modeling the set of feasible building load profiles. In this paper, we consider buildings whose source of flexibility is their HVAC loads. We propose a data-driven method to empirically estimate a robust feasible region of the load using coarse data, that is, using only total building load and average indoor temperatures. The proposed method uses easy-to-gather coarse data and can be adapted to buildings of any type. The resulting feasible region model is robust to temperature prediction errors and is described by linear constraints. The mathematical simplicity of these constraints makes the proposed model adaptable to many power system applications, for example, economic dispatch, and optimal power flow. We validate our model using data from EnergyPlus and demonstrate its usefulness through a case study in which flexible building loads are used to balance errors of wind power forecasts.

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Inverse blackbox modeling of the heating and cooling load in office buildings

Cited by 70 publications

References 27 publications

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

A Probabilistic Algorithm for Predictive Control With Full-Complexity Models in Non-Residential Buildings

Tractable and Robust Modeling of Building Flexibility Using Coarse Data

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