Hybrid Performance Modeling of an Agrophotovoltaic System in South Korea

Kim, Sojung; Kim, Youngjin; On, Youngjae; So, Junyong; Yoon, Changwoo; Kim, Sumin

doi:10.3390/en15186512

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…Nevertheless, those statistical approaches can identify significant variables (or predictors) in the prediction model. Considering that machine learning algorithms are recognized as a black-box approach [67], the limitation can be resolved by integrating the advantages of multiple algorithms.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review

Kim,

Seo,

Kim

2024

Energies

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Biodiesel has received worldwide attention as a renewable energy resource that reduces greenhouse gas (GHG) emissions. Unlike traditional fossil fuels, such as coal, oil, and natural gas, biodiesel made of vegetable oils, animal fats, or recycled restaurant grease incurs higher production costs, so its supply chain should be managed efficiently for operational cost reduction. To this end, multiple machine learning technologies have recently been applied to estimate feedstock yield, biodiesel productivity, and biodiesel quality. This study aims to identify the machine learning technologies useful in particular areas of supply chain management by review of the scientific literature. As a result, nine machine learning algorithms, the Gaussian process model (GPM), random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (KNN), AdaBoost regression, multiple linear regression (MLR), linear regression (LR). and multilayer perceptron (MLP), are used for feedstock yield estimation, biodiesel productivity prediction, and biodiesel quality prediction. Among these, RF and ANN were identified as the most appropriate algorithms, providing high prediction accuracy. This finding will help engineers and managers understand concepts of machine learning technologies so they can use appropriate technology to solve operational problems in supply chain management.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review

Kim,

Seo,

Kim

2024

Energies

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“…Note that its coefficients can be utilized to address the impact of independent variables on a dependent variable (i.e., electricity generation quantity) [1,26]. Moreover, unlike the simple regression model, PR enables the nonlinear relationship between variables to be captured [35]. The JAVAbased PR library proposed by [34] is used with L = 5 in Equation ( 3).…”

Section: Electricity Generation Modelingmentioning

confidence: 99%

Economic Feasibility Comparison between Building-Integrated Photovoltaics and Green Systems in Northeast Texas

Kim

2023

Energies

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Various types of photovoltaic (PV) modules have been developed to exploit solar energy, a major renewable energy resource. One of the popular types of PV modules is building-integrated photovoltaics (BIPV), which are PV modules used as building materials. The goal of this study is to conduct an economic feasibility analysis of BIPV on the rooftop of the Keith D. McFarland Science Building at Texas A&M University, Commerce, Texas. To this end, a polynomial regression (PR) model is developed to estimate the electricity generation quantity of solar energy in the subject area, where the maximum temperature is 104 °F (40 °C) in summer. The developed PR models are used to estimate the potential profit of BIPV on the rooftop of the subject building, and the results are compared with the profit of a green roof system at the subject building. The economic feasibility analyses show that the levelized cost of electricity (LCOE) of the green roof system is approximately 39.77% higher than that of the BIPV system at a discount rate of 5%. Thus, the BIPV system is more profitable than the green roof system; consequently, this research will contribute to the implementation of BIPV on building rooftops and the expansion of renewable energy use in preference to fossil fuel.

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“…Particularly, a country with a land shortage problem such as South Korea faces a trade-off between solar energy production and food security conservation. More specifically, since the Korean government intends to produce 32.2 GW of solar energy by 2030, 425.04 km 2 of land, which is 70% of the land in Seoul, South Korea, needs to be used for solar power plant construction [2]. This implies that approximately 5.16% of rice paddy fields will be converted to solar power plants to produce renewable energy [2].…”

Section: Introductionmentioning

confidence: 99%

A Decision Support Software Application for the Design of Agrophotovoltaic Systems in Republic of Korea

Kim

et al. 2023

Sustainability

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Agrophotovoltaic (APV) systems produce both solar energy and crops, so they are considered a sustainable alternative to traditional solar power plants, which can potentially destroy farmlands. However, it is challenging to diffuse APV systems because of their high installation and operating costs. Thus, to resolve the issue by maximizing the productivity and profits of an APV system, this study aims to propose a mobile-phone-based decision support system (DSS) for a supply chain network design for APV systems in South Korea using satellite imagery incorporating geographic information system (GIS) data. Particularly, polynomial regression models estimating annual corn (Zea mays) yields and the predicted generation of electricity were developed and integrated with the proposed DSS. Field experiment data provided by the APV system at Jeollanamdo Agricultural Research and Extension Services in South Korea were utilized. Two photovoltaic (PV) module types (mono-facial and bi-facial) and three different shading ratios for APV systems (21.3%, 25.6%, and 32.0%) were considered design factors for APV systems. An optimal network structure of 6 candidate APV systems and 15 agricultural markets was devised using the generalized reduced gradient (GRG) method. The profits of the six candidate APV systems are mainly affected by the transportation costs to the markets and the policy of the electricity selling prices. As a result, the proposed supply chain design framework successfully identifies an APV system network with maximum profits from crop production as well as electricity generation.

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Hybrid Performance Modeling of an Agrophotovoltaic System in South Korea

Cited by 10 publications

References 31 publications

Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review

Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review

Economic Feasibility Comparison between Building-Integrated Photovoltaics and Green Systems in Northeast Texas

A Decision Support Software Application for the Design of Agrophotovoltaic Systems in Republic of Korea

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