Research on short-term and ultra-short-term cooling load prediction models for office buildings

Ding, Yan; Zhang, Qiang; Yuan, Tianhao

doi:10.1016/j.enbuild.2017.08.077

Cited by 103 publications

(24 citation statements)

References 40 publications

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“…Yuan et al proposed a sample data selection method based on a grey correlation method integrated with an entropy weight method; the result demonstrated that the accuracy of BPNN had improved [26]. Ding et al divided the sample data by tenfold cross-validation to improve the accuracy of the SVR model in shortterm and ultra-short-term predictions of cooling load [61]. A hybrid SVR was applied to predict the hourly electric demand intensity; the multi-resolution wavelet decomposition was introduced to divide the initial series into several parts, to alleviate the interferential influence on modeling [62].…”

Section: Improved Prediction Modelsmentioning

confidence: 99%

A novel improved model for building energy consumption prediction based on model integration

2020

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Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into "meta-features" to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%-31.6% for Case A and 16.2%-49.4% for Case B), generalization (improvement of 6.7%-29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%-34.1% for Case A and 1.8%-19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

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Section: Improved Prediction Modelsmentioning

confidence: 99%

A novel improved model for building energy consumption prediction based on model integration

2020

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“…With greater moves towards using machine learning in predicting building energy usage, there are many direct comparisons of learning techniques using the same data. However, it is considerably rarer when learning techniques are compared multiple times over multiple ranges [1][2][3][4][5]. With multiple ranges usually occurring when only testing a singular learning technique.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, Support Vector Machines (SVM) are supervised learning models with associated learning algorithms that evaluate data and identify patterns which can be used for regression analysis and classification; first identified by Vladimir Vapnik and his colleagues in 1992 [4,7]. One of it's main advantages, and reasons for it's popularity in predicting building energy usage comes from it's ability to effectively capture and predict nonlinearity [6].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression

Mounter

Dawood

2021

Springer Proceedings in Energy

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Advances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.

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“…Through literature review, it was found that numerous energy demand studies have been conducted on diverse building types, such as commercial buildings ( Yildiz et al, 2017 ), office buildings ( Ding et al, 2017 ), hotel buildings ( Shao et al, 2020 ), and residential buildings ( Gao et al, 2019 ). However, there is a paucity of research that focused on healthcare buildings due to the complexity of their energy consumption patterns.…”

Section: Introductionmentioning

confidence: 99%

Electrical load prediction of healthcare buildings through single and ensemble learning

Cao

Zhang

et al. 2020

Energy Reports

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Healthcare buildings are characterized by complex energy systems and high energy usage, therefore serving as the key areas for achieving energy conservation goals in the building sector. An accurate load prediction of hospital energy consumption is of paramount importance to a successful healthcare building energy management. In this study, eight machine learning models of single learning and ensemble learning were developed for predicting healthcare facilities’ energy consumption. To validate the performance of the proposed model, an experiment was conducted on a general hospital in Shanghai, China. It was found that the two ensemble models, Extreme Gradient Boosting (XGBoost) model and Random Forest (RF) model, outperformed single models in daily electrical load prediction. A further comparison between models trained with daily and weekly temporal resolution electrical data shows that it is more likely to achieve higher accuracy with finer time granularity. Through feature importance analysis, the most influential features under the daily and weekly electrical load prediction were identified. Based on the prediction results, it is expected that hospital facility managers will be able to conveniently assess the expected energy usage of their hospitals with the machine learning models.

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Research on short-term and ultra-short-term cooling load prediction models for office buildings

Cited by 103 publications

References 40 publications

A novel improved model for building energy consumption prediction based on model integration

A novel improved model for building energy consumption prediction based on model integration

The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression

Electrical load prediction of healthcare buildings through single and ensemble learning

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