Predicting commercial wind farm site suitability in the conterminous United States using a logistic regression model

Wimhurst, Joshua J.; Greene, J. Scott; Koch, Jennifer

doi:10.1016/j.apenergy.2023.121880

Cited by 11 publications

(3 citation statements)

References 146 publications

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“…It is a statistical technique involving multiple variables [15] that allows us to analyze the relationship between a group of independent variables and a dichotomous dependent variable. It can only take two values, typically zero and one, where zero represents absence, and one represents presence.…”

Section: Logistic Regressionmentioning

confidence: 99%

Comparing machine learning techniques for software requirements risk prediction

Vera,

Del Carpio

2024

IJEECS

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Software requirements are the most critical phase focused on documenting, eliciting, and maintaining the stakeholders’ requirements. Risk identification and analysis are preemptive actions designed to anticipate and prepare for potential issues. Usually, this classification of risks is done manually, a practice that the personal judgment of the risk analyst or the project manager might influence. Machine learning (ML) techniques were proposed to predict the risk level in software requirements. The techniques used were logistic regression (LR), multilayer perceptron (MLP) neural network, support vector machine (SVM), decision tree (DT), naive bayes, and random forest (RF). Each model was trained and tested using cross-validation with k-folds, each with its respective parameters, to provide optimal results. Finally, they were compared based on precision, accuracy, and recall metrics. Statistical tests were performed to determine if there were significant differences between the different ML techniques used to classify risks. The results concluded that the DT and RF are the techniques that best predict the risk level in software requirements.

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Section: Logistic Regressionmentioning

confidence: 99%

Comparing machine learning techniques for software requirements risk prediction

Vera,

Del Carpio

2024

IJEECS

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“…Saraswat et al [10] looked into the technical, economic, and socio-environmental aspects of solar and wind farm locations in India to determine how suitable they are spatially in relation to the mathematical model. Wimhurst et al [11] introduced a logistic regression model that predicts suitable sites for state-level and nationwide wind energy development in the United States. In the field of offshore wind farms location selection, existing research also mainly adopts the multiple criteria decision making (MCDM) method, multiple attribute decision making (MADM) methods, and mathematical modelling to realize location selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Wind Turbine Location and Wind Energy Resource Evaluation Methodology in Port Scenarios

Huang,

Liu,

Zhong

et al. 2024

Sustainability

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Wind energy is widely distributed in China as a renewable energy source. Aiming to alleviate the issues resulting from fossil fuel consumption faced by developing and developed countries (e.g., climate change) and to meet development needs, this study innovatively proposed methods for the location selection of wind farms and wind turbines in port areas based on the fuzzy comprehensive evaluation method. Considering that the wind turbine location is crucial to wind power generation, this paper focuses on locating wind turbines within a specific set of sea ports. The primary objectives of this paper are to evaluate the potential of wind power generation under different port scenarios and develop a method for assessing the potential of wind energy resources in wind farm areas. Firstly, a method is proposed for identifying the boundaries of wind farms in the port areas and locating wind turbines at sea ports. Furthermore, this study used the National Aeronautics and Space Administration (NASA) wind speed database to test the proposed method with the real-world wind power projects of the Ports of Tianjin, Shanghai, Xiamen, Shenzhen, and Hainan, which are top ports within five major coastal port clusters in China. It is found that the potential power generation capacity of the wind power farms at the above ports is 30.71 GWh, 19.82 GWh, 16.72 GWh, 29.45 GWh, and 24.42 GWh, respectively. Additionally, sensitive results for different types of wind turbines are conducted in the following experiment. The results of this study are fundamental for enriching the research of evaluating wind energy resources of sea ports and promoting the development and use of clean energy in practical environments. Further, the method proposed in this study is essential for optimizing the location and construction of wind turbines, which may help ports in adopting a low-carbon and green development path, thereby mitigating air pollution, and promoting sustainable development.

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“…Sustainability has not been consistently applied to all aspects of wind energy. Given the significant capital investment and large-scale production involved in Wind Turbine Generators (WTG) [10], the scientific community has focused its efforts on applying sustainability criteria to the design and location of wind farms [11][12][13][14], as well as the positioning and design of WTGs [15][16][17]. Over the past decades, sustainability has conventionally been evaluated in terms of the environmental impacts derived from human actions.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Design of Onshore Wind Turbine Foundations

Masanet,

Navarro,

Collado

et al. 2024

J. Phys.: Conf. Ser.

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In recent times, wind power has emerged as a prominent contributor to electricity production. Minimizing the costs and maximizing sustainability of wind energy is required to improve its competitiveness against other non-renewable energy sources. This communication offers a practical approach to assess the sustainability of wind turbine generator foundations from a 3-dimensional holistic point of view. Specifically, the main goal of this study is to analyse the life cycle impacts of one shallow foundation design comparing three different concrete alternatives: conventional concrete, concrete with 66-80% of blast furnace slags and concrete with 20% fly ash, and then to apply a Multi-Criteria Decision-Making model based on TOPSIS method to evaluate and compare the resulting sustainability of each alternative considered. The study results in a methodology for quantifying sustainability rather than simply qualifying it. Therefore, this methodology can be employed for design optimization, such as geometry and materials, with a sustainable perspective in mind. Specifically in this study, concrete with blast furnace slags emerges as the top-ranked sustainable alternative, followed by conventional concrete in second place, and fly ash option in third position.

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Predicting commercial wind farm site suitability in the conterminous United States using a logistic regression model

Cited by 11 publications

References 146 publications

Comparing machine learning techniques for software requirements risk prediction

Comparing machine learning techniques for software requirements risk prediction

Research on Wind Turbine Location and Wind Energy Resource Evaluation Methodology in Port Scenarios

Sustainable Design of Onshore Wind Turbine Foundations

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