Input variable selection for thermal load predictive models of commercial buildings

Kapetanakis, Dimitrios-Stavros; Mangina, Eleni; Finn, Donal

doi:10.1016/j.enbuild.2016.12.016

Cited by 85 publications

(23 citation statements)

References 28 publications

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“…However, Kapetanakis et al [58] also found that the wind speed has no significant correlation to the heating load of the buildings. In the present study, the effect of the sun on the indoor temperature was investigated by calculating correlation between the modelling error RMSE and sunny weather.…”

Section: Discussionmentioning

confidence: 99%

A Dynamic Model for Indoor Temperature Prediction in Buildings

2018

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A novel dynamic model for the temperature inside buildings is presented, aiming to improve energy efficiency by providing predictive information on the heat demand. To analyse the performance and generalizability of the modelling approach, real measurement data was gathered from five different types of buildings. Easily available data from various sources was utilized. The chosen model structure leads to a minimal number of input variables and free parameters. Simulations with real data from five buildings, and applying the identical model structure showed that the average modelling error during the 28-h prediction horizon was constantly below 5%. The results thus demonstrate that the model structure can be standardized and easily applied to predict the indoor temperatures of large buildings. This would finally enable demand side management and the predictive optimization of the heat demand at city level.

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Section: Discussionmentioning

confidence: 99%

A Dynamic Model for Indoor Temperature Prediction in Buildings

2018

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“…Kapetanakis et al utilised the correlation coefficient method to analyse the relationships between the loads of different types of buildings in different regions and various indoor and outdoor variables. The results indicated that when the variables selected by the correlation coefficient method were used as input parameters, the prediction accuracy could be improved and the model complexity could be reduced [31]. Dimension reduction methods such as principal component analysis (PCA) have also been shown to improve the efficiency of load prediction models [32].…”

Section: B Feature Set Contruction Methodsmentioning

confidence: 99%

Ultra-Short-Term Building Cooling Load Prediction Model Based on Feature Set Construction and Ensemble Machine Learning

Ding

Kong

et al. 2020

IEEE Access

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As the requirements for the optimal control of building systems increase, the accuracy and speed of load predictions should also increase. However, the accuracy of load predictions is related to not only the prediction algorithm, but also the feature set construction. Therefore, this study develops a shortterm building cooling load prediction model based on feature set construction. The impacts of four different feature set construction methods-feature extraction, correlation analysis, K-means clustering, and discrete wavelet transform (DWT)-on the prediction accuracy are compared. To ensure that the effect of the feature set construction method is universal, three different prediction algorithms are used. The influences of the sample dimension and prediction time horizon on the prediction accuracy are also analysed. The prediction model is developed based on an ensemble learning algorithm utilising the cubist algorithm, and the performance of the prediction model is improved when DWT is used for constructing the feature set. Compared with other commonly used prediction models, the proposed model exhibits the best performance, with R-squared and CV-RMSE values of 99.8% and 1.5%, respectively. INDEX TERMS Cooling load prediction, Feature extraction, Ensemble learning algorithms, Discrete wavelet transform Nomenclature RF Random forest R-squared Square correlation coefficient GBM Gradient boosting machine FOG First order 5-h gradient DWT Discrete wavelet transform MSG Mean value of the square of the 1-h gradients in the past 5 h CA Correlation analysis FS Feature sets PCA Principal component analysis Large sample FS1 t-SNE t-distributed stochastic neighbour embedding Small sample FS10 g Principal component contribution ratio ARE Absolute value of relative error between predicted and actual values rspearman Spearman rank correlation coefficient IR Degree of improvement in the prediction accuracy Dk Sum of intra-cluster distances At Actual value SSE Sum of the squared error Ft Predicted value MSE Mean square error n Sample size CV-RMSE Root-mean-square error coefficient of variation Reduction ratio Reduction values between two time points

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“…Due to the amount of climatic conditions used in simulation software to predict energy used by a building, there have been attempts [25] to use statistical methods such as the Taguchi method to sieve out the least important climatic factors and thus reduce the number of input parameters that describe weather conditions. In a similar study, Kapetanakis et al [26] used data from Building Energy Management System (BEMS) to examine and subsequently provide appropriate input weather valuables necessary for predicting building heating thermal loads. Different energy prediction software also uses different formats of weather data [27].…”

Section: Weather Data and Energy Predictions Inmentioning

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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Input variable selection for thermal load predictive models of commercial buildings

Cited by 85 publications

References 28 publications

A Dynamic Model for Indoor Temperature Prediction in Buildings

A Dynamic Model for Indoor Temperature Prediction in Buildings

Ultra-Short-Term Building Cooling Load Prediction Model Based on Feature Set Construction and Ensemble Machine Learning

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

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