Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

Al-Shargabi, Amal A.; Almhafdy, Abdulbasit; Ibrahim, Dina M.; Alghieth, Manal; Chiclana, Francisco

doi:10.3390/su132212442

Cited by 10 publications

(1 citation statement)

References 34 publications

(38 reference statements)

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“…These two models were chosen to examine the viability of the developed dataset in predicting the buildings energy consumption in terms of cooling and heating loads. In a recent study of ours ( Al-Shargabi et al, 2021 ), we applied deep learning and created various models to predict the energy consumption of buildings using the dataset described in this study.…”

Section: Methodsmentioning

confidence: 99%

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

Ibrahim

Almhafdy

Al-Shargabi

et al. 2022

PeerJ Computer Science

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Prediction of building energy consumption is key to achieving energy efficiency and sustainability. Nowadays, the analysis or prediction of building energy consumption using building energy simulation tools facilitates the design and operation of energy-efficient buildings. The collection and generation of building data are essential components of machine learning models; however, there is still a lack of such data covering certain weather conditions. Such as those related to arid climate areas. This paper fills this identified gap with the creation of a new dataset for energy consumption of 3,840 records of typical residential buildings of the Saudi Arabia region of Qassim, and investigates the impact of residential buildings’ eight input variables (Building Size, Floor Height, Glazing Area, Wall Area, window to wall ratio (WWR), Win Glazing U-value, Roof U-value, and External Wall U-value) on the heating load (HL) and cooling load (CL) output variables. A number of classical and non-parametric statistical tools are used to uncover the most strongly associated input variables with each one of the output variables. Then, the machine learning Multiple linear regression (MLR) and Multilayer perceptron (MLP) methods are used to estimate HL and CL, and their results compared using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and coefficient of determination (R2) performance measures. The use of the IES simulation software on the new dataset concludes that MLP accurately estimates both HL and CL with low MAE, RMSE, and R2, which evidences the feasibility and accuracy of applying machine learning methods to estimate building energy consumption.

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Section: Methodsmentioning

confidence: 99%

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

Ibrahim

Almhafdy

Al-Shargabi

et al. 2022

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

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Hierarchical multi-scale parametric optimization of deep neural networks

Zhang,

Vassiliadis,

Dorneanu

et al. 2023

Appl Intell

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Traditionally, sensitivity analysis has been utilized to determine the importance of input variables to a deep neural network (DNN). However, the quantification of sensitivity for each neuron in a network presents a significant challenge. In this article, a selective method for calculating neuron sensitivity in layers of neurons concerning network output is proposed. This approach incorporates scaling factors that facilitate the evaluation and comparison of neuron importance. Additionally, a hierarchical multi-scale optimization framework is proposed, where layers with high-importance neurons are selectively optimized. Unlike the traditional backpropagation method that optimizes the whole network at once, this alternative approach focuses on optimizing the more important layers. This paper provides fundamental theoretical analysis and motivating case study results for the proposed neural network treatment. The framework is shown to be effective in network optimization when applied to simulated and UCI Machine Learning Repository datasets. This alternative training generates local minima close to or even better than those obtained with the backpropagation method, utilizing the same starting points for comparative purposes within a multi-start optimization procedure. Moreover, the proposed approach is observed to be more efficient for large-scale DNNs. These results validate the proposed algorithmic framework as a rigorous and robust new optimization methodology for training (fitting) neural networks to input/output data series of any given system. Graphical Abstract

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The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks

Yang

Luo

et al. 2023

Energy

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Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

Cited by 10 publications

References 34 publications

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

Hierarchical multi-scale parametric optimization of deep neural networks

The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks

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