[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128512-en":3,"doc-seo-128512-105":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":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},128512,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning for forecasting a photovoltaic (PV) generation system - Published Version","To mitigate building carbon footprints, on-site renewable energy generation can supply power without relying solely on the national grid. Weather dependence limits renewable reliability, making photovoltaic (PV) forecasting important. This study compares benchmark machine learning algorithms—random forest, neural networks, support vector machines, and linear regression—using datasets sized for different prediction horizons and validated against real-time campus data. Results show random forest achieves the lowest average error, with SVM, linear regression, and neural networks performing progressively worse, and no method is universally best across accuracy and data needs.","Please cite the Published Version  \nScott, Connor, Ahsan, Mominul and Albarbar, Alhussein  (2023) Machine learning for forecasting a photovoltaic (PV) generation system. Energy, 278 . 127807 ISSN 0360-5442  \nDOI: [https://doi.org/10.1016/j.energy.2023.127807](https://doi.org/10.1016/j.energy.2023.127807)  \nPublisher: Elsevier  \nVersion: Published Version  \nDownloaded from: [https://e-space.mmu.ac.uk/632918/](https://e-space.mmu.ac.uk/632918/)  \nUsage rights:  Creative Commons: Attribution 4 .0  \nAdditional Information: This is an open access article which appeared in Energy, published by Elsevier  \nData Access Statement: Data will be made available on request.  \nEnquiries:  \nIf you have questions about this document, contact [rsl@mmu.ac.uk](rsl@mmu.ac.uk. Please)[. Please](rsl@mmu.ac.uk. Please) include the URL of the record in e-space. If you believe that your, or a third party's rights have been compromised through this document please see our Take Down policy (available from [https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines](https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines))  \nEnergy 278 (2023) 127807  \nContents lists available at ScienceDirect  \nEnergy  \njournal [homepage:](homepage: www.elsevier.com/locate/energy)[ www.elsevier.com/locate/energy](homepage: www.elsevier.com/locate/energy)  \n| Machine learning for forecasting a photovoltaic (PV) generation system Connor Scotta, Mominul Ahsan b, *, Alhussein Albarbara\u003Cbr>a Department of Engineering, Manchester Metropolitan University, Chester St, Manchester, M1 5GD, UK b Department of Computer Science, University of York, Deramore Lane, Heslington, York, YO10 5GH, UK |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Handling Editor: X Zhao |  | To mitigate the carbon print of buildings, they should have on-site renewable energy generation systems to supply energy for the buildings without relying on the national grid. Renewable generation sources rely on weather conditions and are therefore difficult to rely on as the only source of energy. Photovoltaic (PV) is forecasted through machine learning algorithms (MLA), but different methods have varied accuracy and have different training requirements such as more inputs or more data in general. No previous research has concluded an optimal MLA but to better apply them to PV systems, this must be established. To conclude an optimal MLA for a particular application, the dataset and required outputs must be determined, and how they affect the performance of the algorithm must be evaluated. The aim of this work is to compare benchmark MLA’s through accuracy and usability for an operational University campus located in central Manchester, in the north of England. The MLA’s including random forest (RF), neural networks (NN), support vector machines (SVM), and linear regression (LR) have been employed to forecast the PV system. If the power output of the renewables is accurately forecasted, a building management system (BMS) can be equipped to optimise on-site renewable energy generation. To accomplish this, sixty-four MLA models are created in total for forecasting at multiple horizons and dataset sizes which are validated against real-time data. Results in this work revealed that the RF algorithms have the lowest average error of the multiple tests at 32 root mean squared error (RMSE), whereas SVM, LR, and NN showed at 32.3 RMSE, 36.5 RMSE, and 38.9 RMSE respectively. Errors between forecasted and actual results are recorded in RMSE whereas changes in error are shown in mean actual percentage error (MAPE) to show the changes with respect to the original value. No MLA outperforms all others for accuracy and for requiring less data. No previous research is conducted to evaluate the performance of various MLA PV forecasting models through various sized data sets with critical analysis on the results. The comparison of benchmark algorithms when forecasti","cbCaid4Gnih8JmAf","https://ap.wps.com/l/cbCaid4Gnih8JmAf","pdf",3369220,1,12,"English","en",105,"# Introduction\n## Background and motivation for PV forecasting\n## Role of building management systems (BMS)\n## Energy decarbonisation and renewable energy reliability\n# Methodology and model comparison\n## Benchmark machine learning algorithms\n## Dataset sizes and multi-horizon forecasting\n# Evaluation metrics and results\n## RMSE and MAPE-based comparison\n## Model performance findings","[{\"question\":\"Why is PV forecasting needed for on-site renewable energy systems?\",\"answer\":\"Renewable generation depends on weather, which makes it difficult to rely on as the only energy source. PV forecasting supports better operational planning for on-site systems and building energy supply.\"},{\"question\":\"Which machine learning algorithms are compared in the study?\",\"answer\":\"The study benchmarks random forest (RF), neural networks (NN), support vector machines (SVM), and linear regression (LR) for PV generation forecasting.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Forecast accuracy is assessed using root mean squared error (RMSE) and mean absolute percentage error (MAPE), comparing predicted outputs against real-time data.\"}]","Machine learning for forecasting a photovoltaic (PV) generation system - Published Version | PDF",1786001477,30,{"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-for-forecasting-a-photovoltaic-pv-generation-system-published-version","",{"@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-for-forecasting-a-photovoltaic-pv-generation-system-published-version/128512/",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","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is PV forecasting needed for on-site renewable energy systems?","Question",{"text":76,"@type":77},"Renewable generation depends on weather, which makes it difficult to rely on as the only energy source. PV forecasting supports better operational planning for on-site systems and building energy supply.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are compared in the study?",{"text":81,"@type":77},"The study benchmarks random forest (RF), neural networks (NN), support vector machines (SVM), and linear regression (LR) for PV generation forecasting.",{"name":83,"@type":74,"acceptedAnswer":84},"How is model performance evaluated?",{"text":85,"@type":77},"Forecast accuracy is assessed using root mean squared error (RMSE) and mean absolute percentage error (MAPE), comparing predicted outputs against real-time data.","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},[94,98,102,106,111,116,121,123,128,131,135],{"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":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":122},"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"]