{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/short-term-load-forecasting-with-deep","title":"Short-term Load Forecasting with Deep Residual Networks","arxiv_id":"1805.11956","date":"2018-05-30","proceeding":null,"authors":["Kunjin Chen","Kunlong Chen","Qin Wang","Ziyu He","Jun Hu","Jinliang He"],"abstract":"We present in this paper a model for forecasting short-term power loads based\non deep residual networks. The proposed model is able to integrate domain\nknowledge and researchers' understanding of the task by virtue of different\nneural network building blocks. Specifically, a modified deep residual network\nis formulated to improve the forecast results. Further, a two-stage ensemble\nstrategy is used to enhance the generalization capability of the proposed\nmodel. We also apply the proposed model to probabilistic load forecasting using\nMonte Carlo dropout. Three public datasets are used to prove the effectiveness\nof the proposed model. Multiple test cases and comparison with existing models\nshow that the proposed model is able to provide accurate load forecasting\nresults and has high generalization capability.","url_abs":"http://arxiv.org/abs/1805.11956v1","url_pdf":"http://arxiv.org/pdf/1805.11956v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"short-term-load-forecasting-with-deep","repo_url":"https://github.com/yalickj/load-forecasting-resnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"load-forecasting","task_name":"Load Forecasting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}