Feature selection for CIE standard sky classification

PurposeThis paper presents the findings from the assessment of the determinants of cloud computing (CC) deployment by construction organisations. Using the technology-organisation-environment (TOE) framework, the study strives to improve construction organisations' project delivery and digital transformation by adopting beneficial technologies like CC.Design/methodology/approachThis study adopted a post-positivism philosophical stance using a deductive approach with a questionnaire administered to construction organisations in South Africa. The data gathered were analysed using descriptive and inferential statistics. Also, the fusion of structural equation modelling (SEM) and machine learning (ML) regression models helped to gain a robust understanding of the key determinants of using CC.FindingsThe study found that the use of CC by construction organisations in South Africa is still slow. SEM indicated that this slow usage is influenced by six technology and environmental factors, namely (1) cost-effectiveness, (2) availability, (3) compatibility, (4) client demand, (5) competitors' pressure and (6) trust in cloud service providers. ML models developed affirmed that these variables have high predictive power. However, sensitivity analysis revealed that the availability of CC and CC's ancillary technologies and the pressure from competitors are the most important predictors of CC usage in construction organisations.Originality/valueThe paper offers a theoretical backdrop for future works on CC in construction, particularly in developing countries where such a study has not been explored.

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“…, 2013), R > 0.8 (Bergqvist et al. , 2020), R > 0.9 (Granados-López et al. , 2021) and R > 0.95 (Zainodin et al.…”

Section: Resultsmentioning

confidence: 99%

Determinants of cloud computing deployment in South African construction organisations using structural equation modelling and machine learning technique

Aghimien

Aigbavboa²,

Chan³

et al. 2022

ECAM

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“…The cloud cover (CC) was calculated with a commercial all-sky camera (SONAD201D) that records every 1 s an RGB color image with 1158 × 1172 pixels of resolution. A complete description of the experimental facility and instruments can be found in previous works [42,43]. Various climatic parameters were recorded, including air temperature (T), relative humidity (RH), wind speed (WS), and direction.…”

Section: Experimental Data and Quality Controlmentioning

confidence: 99%

Ultraviolet Erythemal Irradiance (UVER) under Different Sky Conditions in Burgos, Spain: Multilinear Regression and Artificial Neural Network Models

García-Rodríguez,

Granados-López

et al. 2023

Applied Sciences

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Different strategies for modeling Global Horizontal UltraViolet Erythemal irradiance (GHUVE) based on meteorological parameters measured in Burgos (Spain) have been developed. The experimental campaign ran from September 2020 to June 2022. The selection of relevant variables for modeling was based on Pearson’s correlation coefficient. Multilinear Regression Model (MLR) and artificial neural network (ANN) techniques were employed to model GHUVE under different sky conditions (all skies, overcast, intermediate, and clear skies), classified according to the CIE standard on a 10 min basis. ANN models of GHUVE outperform those based on MLR according to the traditional statistical indices used in this study (R2, MBE, and nRMSE). Moreover, the work proposes a simple all-sky ANN model of GHUVE based on usually recorded variables at ground meteorological stations.

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“…This technique allows determining the correlation of each meteorological measurement with solar radiation [14], correlations that can be different with the season. The prediction accuracy can be statistically evaluated using PCC as a metric; a larger PCC intuitively reflects a higher linear correlation between the predicted and true values [33,45].…”

Section: Feature Selectionmentioning

confidence: 99%

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

et al. 2022

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Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.

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Feature selection for CIE standard sky classification

Cited by 13 publications

References 74 publications

Determinants of cloud computing deployment in South African construction organisations using structural equation modelling and machine learning technique

Determinants of cloud computing deployment in South African construction organisations using structural equation modelling and machine learning technique

Ultraviolet Erythemal Irradiance (UVER) under Different Sky Conditions in Burgos, Spain: Multilinear Regression and Artificial Neural Network Models

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

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