Comparison of physical and machine learning models for estimating solar irradiance and photovoltaic power

Ramadhan, Raden A. A.; Heatubun, Yosca R.J.; Tan, Sek F.; Lee, Hyun-Jin

doi:10.1016/j.renene.2021.06.079

Cited by 53 publications

(14 citation statements)

References 35 publications

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“…They conducted dynamic luminescence measurements by controlling relative humidity and established a predictive model using RNN, demonstrating the potential application of ML in predicting the long-term stability of materials. Ramadhan et al [118] systematically compared physical and ML models' performance in estimating solar irradiance and photovoltaic power. The study selected and compared ML models such as SVR, RF, RNN, gated recurrent unit (GRU), and long shortterm (LSTM) memory with widely used physical models.…”

Section: Materials and Devices Optimizationmentioning

confidence: 99%

“…Comparison of accuracy between ML and physical models on output variables. [118] Battery conversion efficiency can directly impact the performance and availability of batteries. Wasmer et al [119] used ensemble learning to predict solar cell conversion efficiency and explain the impacts of different features on the predictions.…”

Section: Materials and Devices Optimizationmentioning

confidence: 99%

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Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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Optoelectronic materials are essential for today's scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.

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Section: Materials and Devices Optimizationmentioning

confidence: 99%

Section: Materials and Devices Optimizationmentioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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“…It is measured by an instrument called a pyrheliometer; an arrangement of photosensitive elements called thermopile sensors at the base of a light-collimating tube built in a glass-type material. DNI is the most concentrated solar radiation and the most dominant solar irradiance compared to DHI [29]. DNI can be measured as shown in Figure 3.…”

Section: 𝐺𝐻𝐼 = 𝐷𝐻𝐼 + 𝐷𝑁𝐼 •𝑐𝑜𝑠 (𝜃 𝑍 )mentioning

confidence: 99%

Gis-Aided Geographical and Meteorological Data Overview of Solar Radiation Mapping for Malaysia – An Explanatory Study Based on Solar Radiation Prediction Modeling Using Neural Network Approach

2021

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Solar radiation mapping has used geographical and meteorological data. To obtain geographical and meteorological data, a Geographic Information System (GIS) is required. GIS is defined as an integrated geographic resource that presents data in terms of spatial information. This data is important for Neural Networks as it will be used as input parameters for the development of solar radiation prediction models. Solar radiation prediction is one way to map the sun's rays in certain places where there are insufficient resources or space to build a complete solar radiation measurement station. Since predictions about solar radiation require meteorological and geographical data, this paper will give an overview of GIS-assisted geographical and meteorological data to be used as input parameters for solar radiation mapping which will eventually be used as input for prediction models developed for the whole country of Malaysia using Neural Networks. Based on the results, the prediction model developed managed to obtain a coefficient of determination, R2 value of 0.9329.

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“…It is worth mentioning that this research addresses a distinct aspect of solar irradiance estimation, specifically filling the gap in existing literature on estimating the performance of traditional vaulted roofs, which is a novel aspect not extensively explored in the existing literature. While prior attempts have addressed solar irradiance prediction using machine learning, 56,57 they mostly concentrated on general solar exposure estimation. This research is structured as following: The Methodology section highlights the proposed methodology, the Results and Discussion section summarizes and discussions the solar simulation and MLAs results, followed by the Conclusion section that presents overall conclusions of the research.…”

Section: Introductionmentioning

confidence: 99%

Estimating the received solar irradiances by traditional vaulted roofs using optimized neural networks and transfer learning

Ayoub

2023

International Journal of Architectural Computing

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Traditional vaulted roof-forms have long been utilized in hot-desert climate for better indoor environmental quality. Unprecedently, this research investigates the possible contribution of machine learning to estimate the received solar irradiances by those roofs, based on simulation-derived training and testing datasets, where two algorithms were used to reduce their higher-dimensionality. Then, four models of ordinary least-squares and artificial neural networks were developed. Their ability to accurately estimate solar irradiances was confirmed, with R2 of 95.599–98.794% and RMSE of 12.437–23.909 Wh/m2. Transfer Learning was also applied to pass the stored knowledge of the best-performing model into another one for estimating the performance of new roof-forms. The results demonstrated that transferred models could provide better estimations with R2 of 87.416–97.889% and RMSE of 79.300–13.971 Wh/m2, compared to un-transferred models. Machine learning shall redefine the practice of building performance, providing architects with flexibility to rapidly make informed decisions during the early design stages.

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Comparison of physical and machine learning models for estimating solar irradiance and photovoltaic power

Cited by 53 publications

References 35 publications

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Gis-Aided Geographical and Meteorological Data Overview of Solar Radiation Mapping for Malaysia – An Explanatory Study Based on Solar Radiation Prediction Modeling Using Neural Network Approach

Estimating the received solar irradiances by traditional vaulted roofs using optimized neural networks and transfer learning

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