[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121206-en":3,"doc-seo-121206-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"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},121206,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY - Research Abstract","Countries with high insolation face fast growth of solar power plants, yet grid stability and efficient power management remain difficult under weather-driven changes in solar radiation and battery consumption constraints. This study builds a machine learning prediction model using weather data to estimate generated electricity. Four regression algorithms—Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Gradient Boosting Regressor—are evaluated. Random Forest yields the strongest performance, with MAE 0.1114281 and RMSE 0.3187232.","PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY  \nPopong Setiawati1; Adhitio Satyo Bayangkari Karno2; Widi Hastomo3*; Ellya Sestri4; Dian Kasoni5;  \nDodi Arif6; Fahrul Razi7  \nInformatics Engineering1  \nEsa Unggul University, Jakarta, Indonesia 1  \n[https://www.esaunggul.ac.id/](https://www.esaunggul.ac.id/1)[1](https://www.esaunggul.ac.id/1)  \n[setiawatipopong1967@gmail.com](setiawatipopong1967@gmail.com1)[1](setiawatipopong1967@gmail.com1)  \nInformation Systems2, 6  \nGunadarma University, Depok, Indonesia2, 6  \n[https://www.gunadarma.ac.id/](https://www.gunadarma.ac.id/2)[2](https://www.gunadarma.ac.id/2), 6  \n[adh1t10.2@gmail.com](adh1t10.2@gmail.com2)[2](adh1t10.2@gmail.com2), [dodiarif8@gmail.com](dodiarif8@gmail.com6)[6](dodiarif8@gmail.com6)  \nInformation Technology3, 4, 7  \nITB Ahmad Dahlan, Tangerang, Indonesia 3, 4, 7  \n[https://www.itb-ad.ac.id/](https://www.itb-ad.ac.id/3)[3](https://www.itb-ad.ac.id/3), 4, 7  \n[Widie.has@gmail.com](Widie.has@gmail.com3)[3](Widie.has@gmail.com3)*, [ellyasestri24@gmail.com](ellyasestri24@gmail.com4)[4](ellyasestri24@gmail.com4), [fahrulrazi0398@gmail.com](fahrulrazi0398@gmail.com7)[7](fahrulrazi0398@gmail.com7)  \nInformation Systems5  \nSTMIK Antar Bangsa, Tangerang City, Indonesia 5  \n[https://antarbangsa.ac.id/](https://antarbangsa.ac.id/5)[5](https://antarbangsa.ac.id/5)  \n[dhekalearning@gmail.com](dhekalearning@gmail.com5)[5](dhekalearning@gmail.com5)  \n(*) Corresponding Author  \nThe creation is distributed under the Creative Commons Attribution-NonCommercial 4.0 International License.  \nAbstract—In countries with high levels of insolation, the demand for renewable energy sources has driven the rapid emergence and growth of solar powerplants. Maintaining grid stability and efficient power management in response to weather variations that affect solar radiation intensity and battery consumption limits remains a major challenge. This study aims to develop a machine learning-based prediction model to estimate the electricity generated by solar powerplants using weather data. Four algorithms are utilized: Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Gradient Boosting Regressor. The results show that the Random Forest algorithm produces the best model, with MAE and RMSE values of 0.1114281 and 0.3187232, respectively. This research contributes to the literature, particularly on the relatively unexplored topic of using multiple machine learning models to predict energy output from photovoltaic systems. The findings have the potential to inform more efficient energy policies and improve energy integration technologies for grid-connected solar power systems.  \nKeywords: energy forecasting, machine learning, renewable energy.  \nAbstrak—Di negara-negara dengan tingkatinsolasi tinggi, permintaan akan sumber energi terbarukantelah menyebabkan kemunculan dan pertumbuhan pembangkit listrik tenaga surya yang pesat. Mempertahankan stabilitas jaringan dan efektivitasmanajemen daya dalam menghadapi variasi cuaca yang mengubah intensitas radiasi matahari dan batasan konsumsi baterai merupakan tantanganutama. Tujuan dari penelitian ini adalah untuk membuat model prediksi berbasis pembelajaranmesin yang memperkirakan daya listrik yang dihasilkan dari pembangkit listrik tenaga surya menggunakan data cuaca. Penelitian ini menggunakan 4 algoritma yaitu linier regression, random forest regressor, decision tree regressor, gradient boosting regressor. Hasil penelitian menghasilkan model terbaik dari algoritma Random Forest dengan nilai MAE dan RMSE-nya masingmasing adalah 0.1114281 dan 0.3187232. Penelitian ini dapat menambah pengetahuan dalam bidang literatur, terutama berkaitan dengan topik yang belum banyak diteliti tentang penggunaan beberapa  \npembelajaran mesin untuk memprediksi keluaranenergi dari sistem fotovoltaik surya. Hasil studi ini berpotensi memberikan kebijakan energi yang lebihefisien dan teknologi integrasi energ","cbCairjNx4Mn8VDf","https://ap.wps.com/l/cbCairjNx4Mn8VDf","pdf",1670034,1,10,"English","en",105,"# Introduction\n## Renewable energy and solar importance\n## Weather variability and grid stability challenges\n## Limitations of existing forecasting models\n## Motivation for machine learning approaches","[{\"question\":\"What problem does the study address in solar power plants?\",\"answer\":\"It targets the difficulty of maintaining grid stability and efficient power management when weather variations change solar radiation intensity and battery consumption limits.\"},{\"question\":\"Which algorithms are used to predict solar electricity generation?\",\"answer\":\"The study evaluates Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Gradient Boosting Regressor.\"},{\"question\":\"Why is Random Forest the best-performing model in the results?\",\"answer\":\"Random Forest achieves the lowest reported errors, with MAE of 0.1114281 and RMSE of 0.3187232.\"}]","PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY - 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