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    Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis
    (Elsevier, 2023) Çakıroğlu, Celal; Demir, Sercan; Özdemir, Mehmet Hakan; Aylak, Batin Latif; Sariisik, Gencay; Abualigah, Laith
    Wind energy increasingly attracts investment from many countries as a clean and renewable energy source. Since wind energy investment cost is high, the efficiency of a potential wind power plant should be determined using wind power prediction models and wind speed data before installation. Accurate wind power estimation is crucial to set up comprehensive strategies for wind power generation. This study estimated the power produced in a wind turbine using six different regression algorithms based on machine learning using temperature, humidity, pressure, air density, and wind speed data. The proposed estimation model was evaluated on the data received between 2011 and 2020 at station 17,112 in Çanakkale, Turkey. XGBoost, Random Forest, LightGBM, CatBoost, AdaBoost, and M5-Prime algorithms were used to create predictive models. Furthermore, model explanations were presented using the SHAP methodology. Among the regression algorithms evaluated according to the R2 performance metric, the best performance was obtained from the XGBoost algorithm. Regarding computational speed, the LightGBM model emerged as the most efficient model. The wind speed was
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    Öğe
    Readiness and Maturity of Smart and Sustainable Supply Chains: A Model Proposal
    (Taylor & Francis Ltd, 2023) Demir, Sercan; Gunduz, Mehmet Akif; Kayikci, Yasanur; Paksoy, Turan
    Many companies embrace Industry 4.0 technologies to enable operational sustainability against increasing climate change effects, decreasing natural resources, and raising consumer awareness of environmental issues. Even though readiness and maturity assessment of smartness and sustainability concepts are nested, no study simultaneously focuses on these concepts. As pioneering research, we propose a novel model titled Smart and Sustainable Supply chain Readiness and Maturity model (S3RM) and validate it by conducting a case study in the automotive industry. We design our model upon the triple-bottom-line (TBL) approach consisting of smartness and sustainability dime5nsions. Our study introduces the TBL of smartness covering availability, integrity, and adaptability sub-dimension. TBL of sustainability includes social, environmental, and economic sub-dimensions. The proposed model calculates the Smart and Sustainable Readiness and Maturity Index by averaging sustainability scores' summation and smartness scores' multiplication. Each sub-dimension consists of items measured by a readiness and maturity scale. The findings suggest how smartness and sustainability items create strengths, weaknesses, opportunities, and threats for the supply chain operations. Our model provides managerial implications in assessing the readiness and maturity of Industry 4.0 tools and sustainability indicators. This study offers a road map to managers on smart and sustainable supply chains' defined target areas.