[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123280-en":3,"doc-seo-123280-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123280,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","PREDICTION OF SOLAR ENERGY CONTROL SYSTEM BASED ON MACHINE LEARNING METHOD - Master’s Thesis","Accurate solar energy generation prediction is critical for optimizing energy management and supporting the transition to renewable energy. The thesis trains multiple machine learning regression models on real-world solar power plant datasets, using solar generation measurements together with weather sensor and auxiliary information. Predictions are produced for different time intervals, and model performance is evaluated on test datasets. Results indicate strong predictive capability, with R-squared values close to 1 and model comparisons showing decision tree and gradient boosting regressor among the best performers, supported by RMSE analysis.","REPUBLIC OF TÜRKİYE ALTINBAŞ UNIVERSITY Institute of Graduate Studies Electrical and Computer Engineering  \nPREDICTION OF SOLAR ENERGY CONTROL SYSTEM BASED ON MACHINE LEARNING METHOD  \nHasanain Alaa Mohammed ALMINSHID  \nMaster’s Thesis  \nSupervisor  \nAssoc. Prof. Dr. Sefer KURNAZ  \nIstanbul, 2024  \nPREDICTION OF SOLAR ENERGY CONTROL SYSTEM BASED ON  \nMACHINE LEARNING METHOD  \nHasanain Alaa Mohammed ALMINSHID  \nElectrical and Computer Engineering  \nMaster’s Thesis  \nALTINBAŞ UNIVERSITY  \nThe thesis titled C PREDICTION OF SOLAR ENERGY CONTROL SYSTEM BASED ON MACHINE LEARNING METHOD prepared by HASANAIN ALAA MOHAMMED ALMINSHID and submitted on 07/06/2024 has been accepted unanimously for the degree of Master ofEelecrical and Compeuter Engeneering Department.  \nAssoc. Prof. Dr. Sefer KURNAZ  \nSupervisor  \nThesis Defense Committee Members:  \nAssoc. Prof. Dr. Sefer KURNAZ  \nAsst. Prof. Dr. Abdullahi Abdu  \nIBRAHIM  \nDepartment of Computer  \nEngineering,  \nAltınbaş University  \nDepartment of Computer  \nEngineering,  \nAltınbaş University  \n__________________  \n__________________  \nAsst. Prof. Dr. Serdar KARGIN Department of Biomedical  \nEngineering,  \nİstanbul Arel University    \nI hereby declare that this thesis meets all format and submission requirements of a Master’s thesis.  \nI hereby state that all data and material offered in this capstone project were acquired incomplete line with ethical standards and scholastic regulations. I further affirm that, in accordance with the aforementioned standards of academic integrity, all borrowed ideas, arguments, its findings, as well as all the sources indicated in the Reference List, have been correctly cited in the text.  \nHasanain Alaa Mohammed ALMINSHID  \nSignature  \nDEDICATION  \nI would like to dedicate this thesis to my supervisor Assoc. Prof. Dr. sefer KURNAZ and also dedicate this thesis to my father, mother and for my family and relatives that standing beside me and encouraging me to obtain a master's degree. I also thank all my friends and colleagues who supported me.  \nABSTRACT  \nPREDICTION OF SOLAR ENERGY CONTROL SYSTEM BASED ON  \nMACHINE LEARNING METHOD  \nALMINSHID, Hasanain Alaa Mohammed  \nM.Sc., Electrical and Computer Engineering, Altınbaş University  \nSupervisor: Assoc. Prof. Dr. Sefer KURNAZ  \nDate: 06/2024  \nPages: 75  \nAccurate solar energy generation prediction is critical for optimizing energy management and promoting the shift to renewable energy sources. In this research, we use real-world datasets from solar power plants to train several machine learning models to predict solar energy generation. Solar power generating data, weather sensor data, and auxiliary information are among the databases. We anticipate solar energy generation for various time intervals using machine learning methods such as Linear Regression (LR), Random Forest (RF), Decision Tree (DT), Gradient Boosting Regressor (GBR), AdaBoost Regressor (ADBR), and K-Nearest Neighbours (KNN) . Our approach entails training these models on historical data and assessing their performance on test datasets. The findings of this study suggest that machine learning models have promising prediction skills. The R-squared (R2) values for the training and testing datasets demonstrate a good capacity to explain variation in solar energy generation. The DT model, for example, achieved a flawless R2 score of 100% for training datasets and 99.999% for testing datasets. The GBR model came in second, with R2 values of 99.997% for both training and testing. Furthermore, the Root Mean Squared Error (RMSE) values for the GBR model ranged from 1.87 to 109.53 for the KNN model, demonstrating varying degrees of prediction accuracy. In comparison to previous research investigations, our suggested machine learning models regularly show superior prediction accuracy, with R2 scores reaching 99.99% or higher, compared to the best score of 94.50% in previous studies. This study not only advances solar energy  \nprediction using machin","cbCaibboMV0hzei8","https://ap.wps.com/l/cbCaibboMV0hzei8","pdf",1587746,1,80,"English","en",105,"# Abstract\n# Keywords\n# Machine Learning Models and Prediction Method\n## Data Sources\n## Model Training and Evaluation\n## Performance Metrics (R2 and RMSE)","[{\"question\":\"What data sources are used to predict solar energy generation?\",\"answer\":\"The thesis uses real-world solar power plant datasets, including solar power generating data, weather sensor data, and auxiliary information.\"},{\"question\":\"Which machine learning models are used in the prediction approach?\",\"answer\":\"The work trains several regression models: Linear Regression, Random Forest, Decision Tree, Gradient Boosting Regressor, AdaBoost Regressor, and K-Nearest Neighbours.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Models are trained on historical data and assessed on test datasets using R-squared (R2) and error metrics such as Root Mean Squared Error (RMSE).\"},{\"question\":\"What do the results suggest about prediction accuracy compared with prior studies?\",\"answer\":\"The findings show promising accuracy, with R2 values reaching 99.99% or higher, outperforming the best reported previous study score of 94.50%.\"}]","PREDICTION OF SOLAR ENERGY CONTROL SYSTEM BASED ON MACHINE LEARNING METHOD - 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