Optimizing classroom modularity and combinations to enhance daylighting performance and outdoor platform through ANN acceleration in the post-epidemic era

Liu, Yubo; Chen, Kaifan; Ni, Eryu; Deng, Qiaoming

doi:10.1016/j.heliyon.2023.e21598

Heliyon

2023

DOI: 10.1016/j.heliyon.2023.e21598

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Optimizing classroom modularity and combinations to enhance daylighting performance and outdoor platform through ANN acceleration in the post-epidemic era

Yubo Liu,

Kaifan Chen,

Eryu Ni

et al.

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2024

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Cited by 2 publications

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“…A plethora of prediction tools are currently employed to forecast electricity consumption in buildings. These tools encompass a spectrum of methodologies, each of which is designed to address a specific set of needs and to offer a unique set of advantages [1][2][3][4], e.g., machine learning algorithms [11,12], which include support vector machines (SVMs) [7,13,14], artificial neural networks (ANNs) [3,[15][16][17][18], decision trees [3], linear regression [6,15,19,20] and other statistical algorithms [8,12,[21][22][23].…”

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A Comparative Analysis of Polynomial Regression and Artificial Neural Networks for Prediction of Lighting Consumption

Belany,

Hrabovsky,

Sedivy

et al. 2024

Buildings

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This article presents a comparative analysis of two prominent machine learning techniques for predicting electricity consumption in workplace lighting systems: polynomial regression analysis and artificial neural networks. The primary objective is to assess their suitability and applicability for developing an accurate predictive model. After a brief overview of the current state of energy-saving techniques, the article examines several established models for predicting energy consumption in buildings and systems. These models include artificial neural networks, regression analysis and support vector machines. It then focuses on a practical comparison between polynomial regression analysis and an artificial neural network-based model. The article then looks at the data preparation process, outlining how the data is used within each model to establish appropriate prediction functions. Finally, it describes the methods used to evaluate the accuracy of the developed prediction functions. These functions allow the prediction of lighting consumption based on external lighting intensity. The article evaluates the accuracy of the developed prediction functions using the root mean square error, correlation coefficient and coefficient of determination values. The article compares these values obtained for both models, allowing a conclusive assessment of which model provides superior accuracy in predicting lighting consumption based on external lighting intensity.

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confidence: 99%

“…Study [17] proposed a new school building design model for primary and secondary schools in Guangzhou, China, focusing on student health in light of the COVID-19 pandemic. An algorithm considered factors such as daylight autonomy, uniformity, sunlight exposure, platform area, wall length and space utilization to find the optimal building form.…”

mentioning

confidence: 99%

A Comparative Analysis of Polynomial Regression and Artificial Neural Networks for Prediction of Lighting Consumption

Belany,

Hrabovsky,

Sedivy

et al. 2024

Buildings

View full text Add to dashboard Cite

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Multi-Objective Optimization and Sensitivity Analysis of Building Envelopes and Solar Panels Using Intelligent Algorithms

Zhao,

Zhang,

Dong

et al. 2024

Buildings

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The global drive for sustainable development and carbon neutrality has heightened the need for energy-efficient buildings. Photovoltaic buildings, which aim to reduce energy consumption and carbon emissions, play a crucial role in this effort. However, the potential of the building envelope for electricity generation is often underutilized. This study introduces an efficient hybrid method that integrates Particle Swarm Optimization (PSO), Support Vector Machine (SVM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. This integrated approach was used to optimize the external envelope structure and photovoltaic components, leading to significant reductions: overall energy consumption decreased by 41% (from 105 kWh/m2 to 63 kWh/m2), carbon emissions by 34% (from 13,307 tCO2eq to 8817 tCO2eq), and retrofit and operating costs by 20% (from CNY 13.12 million to CNY 10.53 million) over a 25-year period. Sensitivity analysis further revealed that the window-to-wall ratio and photovoltaic windows play crucial roles in these outcomes, highlighting their potential to enhance building energy performance. These results confirm the feasibility of achieving substantial energy savings and emission reductions through this optimized design approach.

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Optimizing classroom modularity and combinations to enhance daylighting performance and outdoor platform through ANN acceleration in the post-epidemic era

Cited by 2 publications

References 30 publications

A Comparative Analysis of Polynomial Regression and Artificial Neural Networks for Prediction of Lighting Consumption

A Comparative Analysis of Polynomial Regression and Artificial Neural Networks for Prediction of Lighting Consumption

Multi-Objective Optimization and Sensitivity Analysis of Building Envelopes and Solar Panels Using Intelligent Algorithms

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