A review and guide on selecting and optimizing machine learning algorithms for daylight prediction

Liu, Qiuping; Chen, Yaodong; Liu, Yang; Lei, Yuanfang; Wang, Yibo; Hu, Pantin

doi:10.1016/j.buildenv.2023.110822

Cited by 8 publications

(5 citation statements)

References 68 publications

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“…The current state of research on lighting prediction is limited to the study of daylight [10][11][12]16,[20][21][22], with the significance of artificial lighting in energy consumption and overall building efficiency being largely overlooked. A more comprehensive approach is therefore required to accurately predict and optimize both natural and artificial lighting usage.…”

Section: Research Problemmentioning

confidence: 99%

“…The value of r ranges from −1 to 1. Equation (11) represents the correlation coefficient [25,26,30,34,40], as follows:…”

Section: Correlation Coefficientmentioning

confidence: 99%

“…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].…”

mentioning

confidence: 99%

“…The predominant focus of existing research in this field lies in either building energy consumption [1,3,[5][6][7]13,14] or the prediction and utilization of daylight availability [10][11][12]16,[20][21][22]. Nevertheless, a reliance on daylight alone is often inadequate.…”

mentioning

confidence: 99%

“…Paper [11] highlighted the increasing use of machine learning (ML) algorithms in the prediction of daylight availability for buildings. It acknowledged the challenges of real-time high-precision prediction due to the complex nature of daylight.…”

mentioning

confidence: 99%

See 4 more Smart Citations

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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