Research on applying machine learning models to predict the electricity generation capacity of rooftop solar energy systems on buildings

Son, Pham Vu Hong; Tran, Huy Dung

doi:10.1007/s42107-023-00722-1

Cited by 3 publications

References 29 publications

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Research on applying machine learning models to predict and assess return on assets (ROA)

Son,

Duong

2024

Asian J Civ Eng

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Return on Assets (ROA), a pro tability measure, is crucial in corporate nance for assessing how e ciently a company uses assets to generate pro t. Currently, the prediction of the ROA index at present is a tedious, manual process. It usually involves making educated guesses or waiting for the accurate data, which becomes available only after nancial reports have been compiled. This paper introduces a machine learning model for predicting the ROA index. The model draws data from 78 companies listed on the Vietnam Stock Exchanges (HOSE and HNX) over the span of 2012 to 2022.The Random Forest (RF) model was put to the test using datasets from selected Vietnamese businesses in 2023. The results demonstrated a high level of precision, with an error rate of less than 1%, an R2 value of 0.9762, and a Root Mean Square Error (RMSE) of 0.5826. These ndings indicate potential real-world uses in predicting and boosting business performance. In conclusion, the integration of machine learning in nancial analysis and prediction represents substantial progress. It enhances both accuracy and e ciency and holds promise for future advancements in nancial management practices. This study aims to encourage more research and development in this area, leading to more advanced and e cient nancial management tools.

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Research on applying machine learning models to predict and assess return on assets (ROA)

Son,

Duong

2024

Asian J Civ Eng

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Machine learning assisted prediction of solar to liquid fuel production: a case study

Shahzad,

Nguyen,

et al. 2024

Process Safety and Environmental Protection

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Predicting Photovoltaic-Thermal Panel Output in Urban Contexts Using Machine Learning Methods

Nazeri,

Taheri,

Zomorodian

2024

Preprint

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In recent years, the use of data-driven methods for predicting photovoltaic (PV) panel electricity generation has grown significantly, with most studies relying on databases of actual PV panel performance. This study introduces a comprehensive methodology for predicting the performance of photovoltaic-thermal (PVT) panels, specifically focusing on electricity generation, hot water production, and carbon reduction. By leveraging artificial intelligence (AI) and machine learning (ML) methods, particularly Artificial Neural Networks (ANN) and Random Forest (RF), this research differentiates itself from prior studies by integrating predictive models for both electrical and thermal outputs. Additionally, the study examines the effect of different installation patterns on PVT panel output. A total of 1,575 different installation configurations were modeled across three urban districts in Tehran, and the results were used to train the two ML algorithms, which were then compared using Pearson correlation coefficient (R²), Root-mean-square deviation (RMSE), and Mean Absolute Error (MAE) metrics. The RF algorithm demonstrated superior performance, achieving an R² accuracy of 0.91 and shorter learning time. Finally, a framework is proposed based on the findings and simulation steps for predicting electricity generation, hot water production, and carbon reduction of PVT systems.

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Research on applying machine learning models to predict the electricity generation capacity of rooftop solar energy systems on buildings

Cited by 3 publications

References 29 publications

Research on applying machine learning models to predict and assess return on assets (ROA)

Research on applying machine learning models to predict and assess return on assets (ROA)

Machine learning assisted prediction of solar to liquid fuel production: a case study

Predicting Photovoltaic-Thermal Panel Output in Urban Contexts Using Machine Learning Methods

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