2017
DOI: 10.1590/s1678-86212017000300165
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Uma comparação de técnicas de aprendizado de máquina para a previsão de cargas energéticas em edifícios

Abstract: Abstractachine learning methods can be used to help design energy-efficient buildings reducing energy loads while maintaining the desired internal temperature. They work by estimating a response from a set of inputs such as building geometry, material properties, project costs, local weather conditions, as well as environmental impacts. These methods require a training phase which considers a dataset drawn from selected variables in the problem domain. This paper evaluates the performance of four machine learn… Show more

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Cited by 25 publications
(6 citation statements)
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“…In contrast, our proposed model is better than the others existing baseline models because, we employed a sequential learning model for nonsequential data which improved the SO and MO performances on both hold-out and 10-fold. Table 7 presents the SO results based on the hold-out technique with recent state-of the-art models [2,4,12,22,23,25,26,28,[31][32][33][34][35][36]44]. For HL prediction, the proposed model (GRU) achieved the least error rates for MAE (0.0102), MSE (0.0003), and RMSE (0.0166).…”
Section: Comparison With State-of-the-art Modelsmentioning
confidence: 99%
“…In contrast, our proposed model is better than the others existing baseline models because, we employed a sequential learning model for nonsequential data which improved the SO and MO performances on both hold-out and 10-fold. Table 7 presents the SO results based on the hold-out technique with recent state-of the-art models [2,4,12,22,23,25,26,28,[31][32][33][34][35][36]44]. For HL prediction, the proposed model (GRU) achieved the least error rates for MAE (0.0102), MSE (0.0003), and RMSE (0.0166).…”
Section: Comparison With State-of-the-art Modelsmentioning
confidence: 99%
“…While accuracy and robustness increase compared to a single tree, interpretability of the model significantly decreases. Furthermore, the accuracy of tree ensembles, such as random forest, is in many cases still inferior to that of other techniques for engineering problems [20,96].…”
Section: Metamodeling Techniquesmentioning
confidence: 99%
“…Artificial Neural Networks (ANN) [2,[9][10][11][12][13][14][15][16][17][18][19]] Decision Trees (DT) [20] Support Vector Regression (SVR) [11,13,14,19,[21][22][23]] Random Forest (RF) and Trees Ensemble [8,13,14,[20][21][22][23][24][25][26][27][28][29] Multi-Layer Perceptron (MLP) [14,20,23,27] Gaussian Mixture Model (GMM) [30] Gradient Boosted Regression Trees (GBRT) [24,31] Extreme Learning Machine (ELM) [10,32,33] Linear Regression (LR) [10,13,21,23,27,34,35] Radial Basis ...…”
Section: Machine Learning Techniques Papersmentioning
confidence: 99%