[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126207-en":3,"doc-seo-126207-105":30,"detail-sidebar-cat-0-en-105":92},{"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":11,"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},126207,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Machine Learning-Based Forecasting Active Power Loss in Distribution Systems - Abstract and Results","The paper presents an ensemble learning framework to forecast active power losses in power distribution networks during the allocation and sizing of distributed generation (DG) units. The model integrates Gradient Boosting Machine Regression (GBMR) to estimate DG location, bus voltages, DG size, and active losses without relying on conventional power flow calculations. Validation on the IEEE 33-bus system confirms practical, adaptable estimation accuracy for grid operations, with GBMR achieving R-squared 0.9997 and very low MAPE 0.2216% plus RMSE 1.0673.","Machine Learning-Based Forecasting Active Power Loss in  \nDistribution Systems  \nWaseem Haider 1,* , Seema Batool2 , Federica Milazzo 1 , and Quang P. Ha 1,*  \n1Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia  \n2Department of Development and Environmental Studies, Paris-Saclay University, Paris, France  \nAbstract. This paper presents an ensemble learning approach to predict the active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The forecast model incorporates the Gradient Boosting Machine Regression (GBMR) to estimate DG location, bus voltages, DG size, and active losses without conventional power flow calculations. The results demonstrate that the suggested estimations of power losses and DG sizing are effective, practical, and adaptable for power system management. The accuracy of the proposed model has been validated using key performance metrics and tested on the standard IEEE 33 bus system. In the case of fixed load, the GBMR outperforms other machine learning techniques with the R-squared 0.9997, with a very low mean absolute percentage error (MAPE) (0.2216%) and a root mean square error (RMSE) of 1.0673 in predicting active power losses. This approach is promising in enabling grid operators to effectively manage DG unit integration of distributed energy resources from precise and reliable estimates of the power loss.  \nKeywords: Distributed Generation, Active Power Loss, Forecasting, Gradient Boosting Machines Regression.  \n1 Introduction  \nTowards the sustainable energy goal, the deployment of distributed energy resources (DERs) is transforming traditional power distribution systems into active distribution networks [1] . Modern electricity distribution systems have, however, encountered severe challenges in integrating to large-scale power grids. This is mainly because of their intermittent nature, which may lead to power quality issues at the consumer end, such as undervoltage, overvoltage, equipment overloading, and control system malfunctions. One of the key criteria for evaluating the efficiency and economy of a power system is the line losses, which indicate the proportion of electrical energy lost due to components such as resistors and inductors during transmission. A higher line loss rate can reduce the overall performance of the power grid. As this loss directly affects both the stability and safety of the system, its minimization is required for optimizing grid operations and economic benefits.  \nTo estimate and examine losses in distributed energy systems, the computational intelligence methodology has been increasingly applied in addition to theoretical calculations. In [2], an association rule method was proposed to extract the characteristics of the network loss sequence and used a forecasting approach for losses with the integration of distributed power. Using artificial neural networks (ANN), a voltage magnitude and line-loading monitoring scheme was introduced in [3] . In [4], an ANN model was  \nutilized to forecast the turbine’s output power over short, medium, and long-term periods. Wind speed and turbine output power are used as inputs, and the output layer predicts the wind turbine’s power output. Grey correlation analysis and neural networks have been proposed in [5] for predicting 10kV line losses.  \nRecently, deep learning has been proposed for power loss estimation. The deep neural network (DNN) in [6] utilized mutiple hidden layers to capture the nonlinear relationship in predicting line losses for large-scale photovoltaic and electric heating losses in low-votage distribution areas. Recurrent neural networks with long short-term memory has been applied to identify the faults leading to power line losses [7] . Machine learning with ensemble techniques has also been suggested. For example, an enhanced random forest approach was developed in [8] to calculate and analyze the theoretical line ","cbCaiiqzPRD7wfDw","https://ap.wps.com/l/cbCaiiqzPRD7wfDw","pdf",720447,1,6,"English","en",105,"# Introduction\n## Motivation and Problem of Line Losses in DER Integration\n## Prior Computational Intelligence and Machine Learning Approaches\n## Motivation for Accurate Active Power Loss Forecasting","[{\"question\":\"What problem does the paper address?\",\"answer\":\"It addresses forecasting active power losses in distribution systems when allocating and sizing distributed generation (DG) units.\"},{\"question\":\"How does the proposed model avoid conventional power flow calculations?\",\"answer\":\"It uses an ensemble learning approach with Gradient Boosting Machine Regression to estimate DG location, bus voltages, DG size, and losses directly from input features.\"},{\"question\":\"How was the method validated and how accurate is it?\",\"answer\":\"The model was tested on the IEEE 33-bus system using performance metrics; for fixed load, it reached R-squared 0.9997, MAPE 0.2216%, and RMSE 1.0673 for predicting active power losses.\"}]","Machine Learning-Based Forecasting Active Power Loss in Distribution Systems - Abstract and Results | PDF",1785903795,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-forecasting-active-power-loss-in-distribution-systems-abstract-and-results","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-forecasting-active-power-loss-in-distribution-systems-abstract-and-results/126207/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address?","Question",{"text":76,"@type":77},"It addresses forecasting active power losses in distribution systems when allocating and sizing distributed generation (DG) units.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed model avoid conventional power flow calculations?",{"text":81,"@type":77},"It uses an ensemble learning approach with Gradient Boosting Machine Regression to estimate DG location, bus voltages, DG size, and losses directly from input features.",{"name":83,"@type":74,"acceptedAnswer":84},"How was the method validated and how accurate is it?",{"text":85,"@type":77},"The model was tested on the IEEE 33-bus system using performance metrics; for fixed load, it reached R-squared 0.9997, MAPE 0.2216%, and RMSE 1.0673 for predicting active power losses.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]