Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques

Olu-Ajayi, Razak; Alaka, Hafiz; Sulaimon, Ismail; Sunmola, Funlade; Ajayi, Saheed

doi:10.1016/j.jobe.2021.103406

Cited by 193 publications

(97 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Mean squared error (MSE) and coefficient of correlation (R-Value) were used to assess the models’ performance [ 109 , 110 ], as shown in Equations (1) and (2), respectively.

where

, and

are the experimental values setup and model domain.…”

Section: Methodsmentioning

confidence: 99%

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

View full text Add to dashboard Cite

A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques—Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)—to estimate the 28-day compressive strength (f’c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f’c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%).

show abstract

“…Mean squared error (MSE) and coefficient of correlation (R-Value) were used to assess the models’ performance [ 109 , 110 ], as shown in Equations (1) and (2), respectively.

where

, and

are the experimental values setup and model domain.…”

Section: Methodsmentioning

confidence: 99%

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

View full text Add to dashboard Cite

show abstract

“…Gradient boosting (GB) is a supervised ML method used for both regression and clas sification problems [54,55]. It was designed in 2001 by Friedman [56] as a method tha combines a set of weak models to form a more robust model using additive models.…”

Section: Gradient Boosting (Gb)mentioning

confidence: 99%

“…Four metrics were used to evaluate the performance of the models: coefficient of determination (R 2 ) (Equation ( 2)), root mean square error (RMSE) (Equation ( 3)), and mean absolute error (MAE) (Equation ( 4)). These metrics estimate errors in the predictions of the splitting tensile strength (of the SCC made with RA after 28 days) when compared with actual observations [9,53,55,97].…”

Section: Model Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

View full text Add to dashboard Cite

Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R2 values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R2 value of R2 = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement.

show abstract

“…Using the ANN tool to develop the neural network; the models' performance was assessed using two measures; coefficient of correlation (R-value) and mean squared error (MSE) [96,97], as given in Equation (1).…”

Section: Ann Network Model Evaluationmentioning

confidence: 99%

Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

et al. 2022

View full text Add to dashboard Cite

The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the prediction of concrete properties using soft computing techniques, which will eventually reduce environmental degradation and other material waste. There have been very limited and contradicting studies regarding prediction using different ANN algorithms. This paper aimed to predict the 28-day splitting tensile strength of SCC with RA using the artificial neural network technique by comparing the following algorithms: Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB). There have been very limited and contradicting studies regarding prediction by using and comparing different ANN algorithms, so a total of 381 samples were collected from various published journals. The input variables were cement, admixture, water, fine and coarse aggregates, and superplasticizer; the data were randomly divided into three sets—training (60%), validation (10%), and testing (30%)—with 10 neurons in the hidden layer. The models were evaluated by the mean squared error (MSE) and correlation coefficient (R). The results indicated that all three models have optimal accuracy; still, BR gave the best performance (R = 0.91 and MSE = 0.2087) compared with LM and SCG. BR was the best model for predicting TS at 28 days for SCC with RA. The sensitivity analysis indicated that cement (30.07%) was the variable that contributed the most to the prediction of TS at 28 days for SCC with RA, and water (2.39%) contributed the least.

show abstract

Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques

Cited by 193 publications

References 64 publications

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

Contact Info

Product

Resources

About