A soft computing method for the prediction of energy performance of residential buildings

Nilashi, Mehrbakhsh; Dalvi-Esfahani, Mohammad; Ibrahim, Othman; Bagherifard, Karamollah; Mardani, Abbas; Zakuan, Norhayati

doi:10.1016/j.measurement.2017.05.048

Cited by 40 publications

(25 citation statements)

References 34 publications

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“…They indicated the performance of multiple regression was better than simple regression ones in forecasting the BTS. Researchers of this field of study have proposed not only several empirical models, but also lots of intelligent systems to solve science and engineering problems (e.g., Singh et al [34]; Yılmaz and Yuksek [35]; Hajihassani et al [36]; Koopialipoor et al [37]; Nilashi et al [38]; Milovančević et al [39]; Roy et al [40]; Roy and Singh [41]; Keshtegar et al [42]), particularly the geotechnical engineering ones (e.g., Mohamad et al [43]; Nanda et al [44]). Furthermore, right to the point we are focused in this study, a number of studies can be found in literature.…”

Section: Introductionmentioning

confidence: 99%

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

et al. 2020

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Tensile strength (TS) of rock is one of the important properties in design process of construction civil works such as foundations and tunnels. Brazilian tensile strength (BTS) or splitting test is considered as a well-known method in evaluating TS. The present study attempts to propose a novel metaheuristic approach for the indirect measurement of BTS. This new approach is based on the firefly algorithm (FA) for training and optimizing the consequent parameters of the adaptive neuro-fuzzy inference system (ANFIS). The rock samples collected from a tunnel in Malaysia were tested in the laboratory for the purpose of providing a database consisting of totally 80 samples for analysis. Then, the statistical metrics such as coefficient of determination (R 2) were used to examine the acceptability of the proposed ANFIS-FA and ANFIS models. Finally, it was concluded that the ANFIS-FA (with the R 2 of 0.982) can be effectively used as a robust model to predict BTS. INDEX TERMS Tensile strength, brazilian tensile strength, ANFIS, firefly algorithm.

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Section: Introductionmentioning

confidence: 99%

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

et al. 2020

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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%

Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

Sajjad

Khan

et al. 2020

Sensors

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In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models.

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“…For this reason, an ANN is one of the most appropriate machine-learning methods for the current dataset. According to Table 1, some papers specifically applied ANN to the dataset (Ahmed et al [20]; Nwulu [28]), while others applied the ensemble approach by incorporating ANNs (Chou and Bui [18]; Sonmez et al [21]; Naji et al [25]; Nilashi et al [27]). To the best of the authors' knowledge, Sekha et al [4] is the only paper that applied DNNs to forecast HL and CL.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Another SA technique is principal component analysis (PCA), which identifies significant inputs in a dataset, reduces the dimension of the data, and eliminates the problem of multi-collinearity. Nilashi et al [27] states that PCA has four objectives: extracting important information, compressing data, simplifying the descriptions, and analyzing the structure of observations. According to Table 1, most of the reviewed papers did not apply any SA techniques on the dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks

et al. 2020

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As the level of greenhouse gas emissions increases, so does the importance of the energy performance of buildings (EPB). One of the main factors to measure EPB is a structure’s heating load (HL) and cooling load (CL). HLs and CLs depend on several variables, such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. This research uses deep neural networks (DNNs) to forecast HLs and CLs for a variety of structures. The DNNs explored in this research include multi-layer perceptron (MLP) networks, and each of the models in this research was developed through extensive testing with a myriad number of layers, process elements, and other data preprocessing techniques. As a result, a DNN is shown to be an improvement for modeling HLs and CLs compared to traditional artificial neural network (ANN) models. In order to extract knowledge from a trained model, a post-processing technique, called sensitivity analysis (SA), was applied to the model that performed the best with respect to the selected goodness-of-fit metric on an independent set of testing data. There are two forms of SA—local and global methods—but both have the same purpose in terms of determining the significance of independent variables within a model. Local SA assumes inputs are independent of each other, while global SA does not. To further the contribution of the research presented within this article, the results of a global SA, called state-based sensitivity analysis (SBSA), are compared to the results obtained from a traditional local technique, called sensitivity analysis about the mean (SAAM). The results of the research demonstrate an improvement over existing conclusions found in literature, which is of particular interest to decision-makers and designers of building structures.

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A soft computing method for the prediction of energy performance of residential buildings

Cited by 40 publications

References 34 publications

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks

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