A machine learning approach to modelling solar irradiation of urban and terrain 3D models

Vartholomaios, Aristotelis

doi:10.1016/j.compenvurbsys.2019.101387

Cited by 29 publications

(10 citation statements)

References 39 publications

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“…Aided by AI technologies, MVF or related precision agriculture technologies, have the potential to increase agricultural outputs. Vartholomaios (2019) explored the capability of a Gradient Boosting ML algorithm to predict solar irradiation. The algorithm captured the non‐linear relationship between solar irradiation and the Sky View Factor together with dome surface orientation.…”

Section: Ai Applications In Soil Management and Agricultural Productionmentioning

confidence: 99%

AI‐enhanced soil management and smart farming

Chen

Chong³

et al. 2021

Soil Use and Management

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Soil and crop management are facing challenges, in particular an increasing pressure to feed a growing population and improve food safety. Smart farming assists in dealing with these issues by incorporating information and communication technologies into farming practice. The Multiponics Vertical Farming (MVF) system is a very promising concept in smart farming that saves space, transportation cost and delivery time. Complicated systems and messy data can be processed by advanced technologies to achieve greater accuracy and efficiency in modelling and computing. For dynamic and complex problems, Artificial Intelligence (AI) provides rapid evaluation and aids in designing effective solutions. This study discusses how AI is adopted in soil management and MVF for tasks including classification, detection and forecasting. Three categories of AI and machine learning techniques, Support Vector Machine (SVM), Decision Tree (DT) and Neural Network (NN)‐based models, that are widely used will be presented. Future perspectives on urban farming are also discussed.

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Section: Ai Applications In Soil Management and Agricultural Productionmentioning

confidence: 99%

AI‐enhanced soil management and smart farming

Chen

Chong³

et al. 2021

Soil Use and Management

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“…ML can predict out of precedent simulation results, how the new building would behave. ML has been introduced in building engineering to accelerate complex simulation and predict energy consumption [21,22,25], solar radiation [39], daylight availability [3], thermal comfort [6,15,27] or to predict urban heat island phenomena [16]. ML has been used also for the simulation and prediction of other mechanical aspects like material behavior [33].…”

Section: Machine Learning For Architectural Designmentioning

confidence: 99%

Machine learning for optimized buildings morphosis

Raboudi

Saci

2020

Proceedings of the 2nd International Conference on Digital Tools &Amp; Uses Congress

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The world is rapidly urbanizing, with an increasing number of new building constructions. This involves increasing the world's energy consumption and its associated greenhouse gas emissions. Computational tools are playing an increasing impact on the architectural design process. Recently, Machine learning (ML) has been applied to building design and has evinced its potential to improve building performance. This paper tries to review the use of ML for the building morphosis. We then forecast the use of machine learning for building optimized morphosis in the early design stage particularly for ensuring summer shading and winter solar access between neighbors.

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“…To accurately estimate data on incident solar radiation in mountainous regions, a comprehensive analysis of the shading effects of complex terrain on solar radiation is imperative. The sky-view factor (SVF) is a common parameter used to quantify terrain shading effects on solar radiation [12][13][14][15][16]. SVF is defined as the ratio of the unobstructed surface area to the entire surface area of the hemispherical sky [17].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Estimating Solar Radiation Shading Rates in Mountainous Areas

Xu,

Li,

Wang

et al. 2024

Sustainability

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Quantification of shading effects from complex terrain on solar radiation is essential to obtain precise data on incident solar radiation in mountainous areas. In this study, a machine learning (ML) approach is proposed to rapidly estimate the shading effects of complex terrain on solar radiation. Based on two different ML algorithms, namely, Ordinary Least Squares (OLS) and Gradient Boosting Decision Tree (GBDT), this approach uses terrain-related factors as input variables to model and analyze direct and diffuse solar radiation shading rates. In a case study of western Sichuan, the annual direct and diffuse radiation shading rates were most correlated with the average terrain shading angle within the solar azimuth range, with Pearson correlation coefficients of 0.901 and 0.97. The GBDT-based models achieved higher accuracy in predicting direct and diffuse radiation shading rates, with R2 values of 0.982 and 0.989, respectively, surpassing the OLS-based models by 0.081 and 0.023. In comparisons between ML models and classic curve-fitting models, the GBDT-based models consistently performed better in predicting both the direct radiation shading rate and the diffuse radiation shading rate, with a standard deviation of residuals of 0.330% and 0.336%. The OLS-based models also showed better performance compared to the curve-fitting models.

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A machine learning approach to modelling solar irradiation of urban and terrain 3D models

Cited by 29 publications

References 39 publications

AI‐enhanced soil management and smart farming

AI‐enhanced soil management and smart farming

Machine learning for optimized buildings morphosis

A Machine Learning Approach to Estimating Solar Radiation Shading Rates in Mountainous Areas

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