[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122441-en":3,"doc-seo-122441-105":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},122441,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Edge Machine Learning for Solar Power Forecasting - Research and Evaluation","The integration of edge computing and machine learning (ML) is transforming energy forecasting by enabling adaptive, real-time analysis as renewable generation and energy demand fluctuate. Traditional cloud-based approaches often face high computation needs, network latency, and reliance on stable connectivity, limiting timely decision support. This work evaluates data stream ML models designed for edge, cloud, and IoT contexts, addressing concept drift, computational cost, and performance trade-offs. Validation uses real-world solar power data from South Wales and New Zealand energy market pricing to improve forecast accuracy, integration, and sustainability through edge-based intelligence.","ORCA – Online Research @  \nCardiff  \nThis is an Open Access document downloaded from ORCA, Cardiff University's institutional repository:[https://orca.cardiff.ac.uk/id/eprint/181889/](https://orca.cardiff.ac.uk/id/eprint/181889/)  \nThis is the author’s version of a work that was submitted to / accepted for publication.  \nCitation for final published version:  \nCassales, Guilherme Weigert, Petri, Ioan , Gomes, Heitor Murilo, Rana, Omer and Bifet, Albert 2025. Edge machine learning for solar power forecasting. Presented at: 12th International Conference on Future Internet of Things and Cloud (FiCloud), Istanbul, Turkiye, 11-13 August 2025. Proceedings of the 12th International Conference on Future Internet of Things and Cloud. IEEE, pp. 84-91. 10.1109/FiCloud66139.2025.00020  \nPublishers page: [https://doi.org/10.1109/FiCloud66139.2025.00020](https://doi.org/10.1109/FiCloud66139.2025.00020)  \nPlease note:  \nChanges made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher’s version if you wish to cite this paper.  \nThis version is being made available in accordance with publisher policies. See [http://orca.cf.ac.uk/policies.html](http://orca.cf.ac.uk/policies.html) for usage policies. Copyright and moral rights for publications made  \navailable in ORCA are retained by the copyright holders.  \nEdge Machine Learning for Solar Power  \nForecasting  \nGuilherme Weigert Cassales  \nAI Institute University of Waikato Hamilton, New Zealand [guilherme.cassales@waikato.ac.nz](guilherme.cassales@waikato.ac.nz)  \nOmer Rana  \nSchool of Computer Science and Informatics Cardiff University Cardiff, South Wales [RanaOF@cardiff.ac.uk](RanaOF@cardiff.ac.uk)  \nIoan Petri  \nSchool of Engineering Cardiff University Cardiff, South Wales [PetriI@cardiff.ac.uk](PetriI@cardiff.ac.uk)  \nHeitor Murilo Gomes  \nSchool of Engineering and Computer Science Victoria University of Wellington Wellington, New Zealand [heitor.gomes@vuw.ac.nz](heitor.gomes@vuw.ac.nz)  \nAlbert Bifet  \nAI Institute University of Waikato Hamilton, New Zealand [abifet@waikato.ac.nz](abifet@waikato.ac.nz)  \nAbstract—The integration of edge computing and machine learning (ML) in energy forecasting marks a transformative shift in optimizing energy systems. As energy demands fluctuate and renewable adoption grows, traditional forecasting methods struggle to adapt. By deploying ML algorithms for data streamson edge devices, it becomes possible to analyse large datasets in real time, uncovering complex patterns that improve forecast accuracy and reliability.  \nThe emergence of energy-edge orchestration, which supports continuous and efficient edge operation, further drives the need for edge-based forecasting, particularly in industrial processes powered by renewables like solar and wind. Local data processing reduces latency, lowers energy use, and enables real-time decisions for smart grids and predictive maintenance.  \nThis paper evaluates data stream ML models optimized for cloud and IoT settings, tackling challenges like concept drift, computational cost, and performance penalty. Unlike many deep learning approaches, our models maintain accuracy with reduced complexity, making them suitable for resource-constrained devices. We validate this on real-world solar power data from South Wales and energy market pricing from New Zealand, demonstrating improved renewable energy integration and sustainability through edge-based intelligence.  \nIndex Terms—Real-time Edge systems, Machine learning, Data Streams, Energy Grids  \nI. INTRODUCTION  \nThe increasing reliance on renewable energy sources such as solar and wind power introduces challenges in energy management due to their inherent variability. Traditional cloudbased forecasting methods often struggle to deliver timely and efficient predictions because","cbCairYQDWxUb5kZ","https://ap.wps.com/l/cbCairYQDWxUb5kZ","pdf",658747,1,9,"English","en",105,"# Abstract\n# Introduction\n## Challenges in renewable energy forecasting\n## Edge ML as an alternative\n## Motivation from real-world disruptions","[{\"question\":\"Why do traditional cloud-based forecasting methods struggle for renewable energy?\",\"answer\":\"They often face high computational demands, network latency, and dependence on stable internet connections, which limits timely predictions under rapidly changing conditions.\"},{\"question\":\"What advantage does edge machine learning provide in energy forecasting?\",\"answer\":\"Edge ML enables real-time local data processing, reducing latency and energy use while supporting adaptive handling of dynamic patterns and concept drift.\"},{\"question\":\"How does the paper validate its data stream ML approach?\",\"answer\":\"It evaluates models using real-world solar power data from South Wales and energy market pricing data from New Zealand, demonstrating improved renewable energy integration and forecasting outcomes.\"}]","Edge Machine Learning for Solar Power Forecasting - 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