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This study compares Machine Learning and Neuro-Fuzzy methods for predicting energy output at the Sengguruh Hydropower Plant, Indonesia. Using one year of engineering-validated data and evaluating ANN, RF, SVR, and ANFIS with RMSE, MAE, and R², Random Forest achieves the highest accuracy, while ANFIS offers better interpretability.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comparing-machine-learning-and-neuro-fuzzy-models-for-hydropower-prediction-under-seasonal-operating-conditions/290656/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/comparing-machine-learning-and-neuro-fuzzy-models-for-hydropower-prediction-under-seasonal-operating-conditions/290656.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What methods does the study compare for hydropower prediction?","Question",{"text":113,"@type":114},"It compares three Machine Learning models—ANN, Random Forest (RF), and Support Vector Regression (SVR)—against an Adaptive Neuro-Fuzzy Inference System (ANFIS).","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What data and evaluation metrics are used?",{"text":118,"@type":114},"The models are developed and evaluated using one year of validated operational data, and performance is measured with RMSE, MAE, and R².",{"name":120,"@type":111,"acceptedAnswer":121},"How do the results differ between Machine Learning and ANFIS?",{"text":122,"@type":114},"Machine Learning models deliver superior predictive performance, with RF the most accurate, while ANFIS yields lower accuracy but provides more interpretable hydropower operating behavior.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290656,1789642279,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Comparing Machine Learning and Neuro-Fuzzy Models for Hydropower Prediction Under Seasonal Operating  \nConditions  \n1Daniel Rohi,2Akhmad Solikin  \n1,2Department of Electrical Engineering, PGRI Adi Buana University, Surabaya, Indonesia  \n[rohi@adibuanasby.ac.id](rohi@adibuanasby.ac.id)  \nAbstract-Accurate prediction of hydropower energy production is essential for operational planning and decision-making under varying hydrological conditions. However, comparative evaluations of high-accuracy data-driven models and interpretable intelligent models using real operational data remain limited. This study aims to compare the performance of Machine Learning and Neuro-Fuzzy approaches for predicting energy production at the Sengguruh Hydropower Plant, Indonesia. One year of engineering-validated operational data consisting of discharge, reservoir and tailrace water levels, operation time, turbine efficiency, and derived hydraulic head were used for model development and evaluation. Three Machine Learning models, namely Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Regression (SVR), were compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results indicate that Machine Learning models achieved superior predictive performance, with Random Forest providing the highest accuracy (RMSE = 8.42 MWh/day, MAE = 6.35 MWh/day, and R² = 0.982). In contrast, the ANFIS model produced lower prediction accuracy but offered a more interpretable representation of hydropower operating behavior. Correlation analysis further indicates that operation time and inflow discharge are the most influential operational variables associated with energy production. The findings demonstrate that Machine Learning approaches are more suitable for highaccuracy forecasting and operational optimization, whereas Neuro-Fuzzy models provide advantages in interpretability and operational representation. These results contribute to the development of intelligent hydropower forecasting systems that balance predictive performance and engineering understanding.  \nKeywords — Hydropower, Machine Learning, Neuro-Fuzzy, Random Forest, seasonal hydrology, operational modeling.  \nI. Introduction  \nThis paper investigates the application of Machine Learning and Neuro-Fuzzy approaches for hydropower energy prediction using real operational data from the Sengguruh Hydropower Plant.  \nHydropower generation is fundamentally governed by hydraulic and energy conversion principles. The power output depends on discharge and head, which are the dominant  \nvariables in energy production [21] . The power output of a hydropower plant can be expressed as:  \nP=ρgQHη (1)  \nWhere P = generated power (W),ρ = water density (kg/m³),g = gravitational acceleration (9.81 m/s²), Q = water discharge (m³/s), H = net head (m) and η = overall system efficiency. This equation highlights that hydropower output is directly influenced by discharge and head, which are the dominant variables in energy production.  \nIn practice, however, system efficiency varies with operating conditions, and hydrological inputs are highly dynamic. As a result, purely physics-based models are often insufficient to represent real operating behavior, particularly under nonlinear and uncertain conditions.  \nTo overcome the limitations of conventional models, Machine Learning (ML) approaches have been widely adopted in hydropower modeling. These methods learn the relationship between input variables and energy output directly from data without requiring explicit physical equations, enabling effective representation of complex nonlinear system behavior.  \nRecent studies have demonstrated that ML models achieve high predictive accuracy in hydropower systems [1],[2], [7], [15] . In particular, ensemble learning methods such as Random Forest and gradient boosting are ","cbCaisQzfPsnhmD2","https://ap.wps.com/l/cbCaisQzfPsnhmD2","pdf",832441,"English","# Introduction\n## Hydropower power relation and modeling challenge\n## Machine Learning for hydropower prediction\n## Neuro-Fuzzy and ANFIS interpretability","[{\"question\":\"What methods does the study compare for hydropower prediction?\",\"answer\":\"It compares three Machine Learning models—ANN, Random Forest (RF), and Support Vector Regression (SVR)—against an Adaptive Neuro-Fuzzy Inference System (ANFIS).\"},{\"question\":\"What data and evaluation metrics are used?\",\"answer\":\"The models are developed and evaluated using one year of validated operational data, and performance is measured with RMSE, MAE, and R².\"},{\"question\":\"How do the results differ between Machine Learning and ANFIS?\",\"answer\":\"Machine Learning models deliver superior predictive performance, with RF the most accurate, while ANFIS yields lower accuracy but provides more interpretable hydropower operating behavior.\"}]","Comparing Machine Learning and Neuro-Fuzzy Models for Hydropower Prediction Under Seasonal Operating Conditions | PDF",23]