{"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/multi-time-horizon-solar-forecasting-using","title":"Multi-time-horizon Solar Forecasting Using Recurrent Neural Network","arxiv_id":"1807.05459","date":"2018-07-14","proceeding":null,"authors":["Sakshi Mishra","Praveen Palanisamy"],"abstract":"The non-stationarity characteristic of the solar power renders traditional\npoint forecasting methods to be less useful due to large prediction errors.\nThis results in increased uncertainties in the grid operation, thereby\nnegatively affecting the reliability and increased cost of operation. This\nresearch paper proposes a unified architecture for multi-time-horizon\npredictions for short and long-term solar forecasting using Recurrent Neural\nNetworks (RNN). The paper describes an end-to-end pipeline to implement the\narchitecture along with the methods to test and validate the performance of the\nprediction model. The results demonstrate that the proposed method based on the\nunified architecture is effective for multi-horizon solar forecasting and\nachieves a lower root-mean-squared prediction error compared to the previous\nbest-performing methods which use one model for each time-horizon. The proposed\nmethod enables multi-horizon forecasts with real-time inputs, which have a high\npotential for practical applications in the evolving smart grid.","url_abs":"http://arxiv.org/abs/1807.05459v1","url_pdf":"http://arxiv.org/pdf/1807.05459v1.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":"multi-time-horizon-solar-forecasting-using","repo_url":"https://github.com/sakshi-mishra/solar-forecasting-RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-anomaly-detection-and-segmentation","task_name":"3D Anomaly Detection and Segmentation"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.05459","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}