Rapid quantification of demand response potential of building HAVC system via data-driven model

Zhu, J.; Niu, Jide; Tian, Zhe; Zhou, Ruoyu; Ye, Chuang

doi:10.1016/j.apenergy.2022.119796

Cited by 13 publications

(2 citation statements)

References 48 publications

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“…Furthermore, the results of the conducted comparisons show that key influencing factors are crucial for increasing calculation accuracy. Increasing the number of input parameters may introduce interference, which decreases the accuracy of the results obtained [69,70]. Thus, the coupling relationship between different input quantities and algorithm performances will be further explored.…”

Section: Discussionmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

Lu,

Cui,

Gao

et al. 2024

Buildings

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Machine learning algorithms have proven to be practical in a wide range of applications. Many studies have been conducted on the operational energy consumption and thermal comfort of radiant floor systems. This paper conducts a case study in a self-designed experimental setup that combines radiant floor and fan coil cooling (RFCFC) and develops a data monitoring system as a source of historical operational data. Seven machine learning algorithms (extreme learning machine (ELM), convolutional neural network (CNN), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM)) were employed to predict the behavior of the RFCFC system. Corresponding prediction models were then developed to evaluate operative temperature (Top) and energy consumption (Eh). The performance of the model was evaluated using five error metrics. The obtained results showed that the RF model had very high performance in predicting Top and Eh, with high correlation coefficients (>0.9915) and low error metrics. Compared with other models, it also demonstrated high accuracy in Eh prediction, yielding maximum reductions of 68.1, 82.4, and 43.2% in the mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), respectively. A sensitivity ranking algorithm analysis was also conducted. The obtained results demonstrated the importance of adjusting parameters, such as the radiant floor supply water temperature, to enhance the indoor comfort. This study provides a novel and effective method for evaluating the energy efficiency and thermal comfort of radiant cooling systems. It also provides insights for optimizing the efficiency and thermal comfort of RFCFC systems, and lays a theoretical foundation for future studies integrating machine learning algorithms in this field.

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Section: Discussionmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

Lu,

Cui,

Gao

et al. 2024

Buildings

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“…This method significantly streamlines the building energy consumption assessment process. Yet, much of the current research is centered on the building O&M process, notably optimizing HVAC systems by predicting dynamic building loads (Ahmad et al, 2016;Zhu et al, 2022). Some studies have highlighted the use of data-driven methods for predicting building energy use intensity early in the design phase (M. Wang et al, 2022aWang et al, , 2022bWang et al, , 2022c.…”

Section: Methods For Estimating Energy Consumption In Urban Buildingsmentioning

confidence: 99%

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Jiang,

Huang,

et al. 2024

FURP

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Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error.

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Model-based investigation on building thermal mass utilization and flexibility enhancement of air conditioning loads

Sun,

Zhao,

Lyu

2024

Build. Simul.

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Rapid quantification of demand response potential of building HAVC system via data-driven model

Cited by 13 publications

References 48 publications

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Model-based investigation on building thermal mass utilization and flexibility enhancement of air conditioning loads

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