{"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/a-regressive-convolution-neural-network-and","title":"A Regressive Convolution Neural network and Support Vector Regression Model for Electricity Consumption Forecasting","arxiv_id":"1810.08878","date":"2018-10-21","proceeding":null,"authors":["Youshan Zhang","Qi Li"],"abstract":"Electricity consumption forecasting has important implications for the\nmineral companies on guiding quarterly work, normal power system operation, and\nthe management. However, electricity consumption prediction for the mineral\ncompany is different from traditional electricity load prediction since mineral\ncompany electricity consumption can be affected by various factors (e.g., ore\ngrade, processing quantity of the crude ore, ball milling fill rate). The\nproblem is non-trivial due to three major challenges for traditional methods:\ninsufficient training data, high computational cost and low prediction\naccu-racy. To tackle these challenges, we firstly propose a Regressive\nConvolution Neural Network (RCNN) to predict the electricity consumption. While\nRCNN still suffers from high computation overhead, we utilize RCNN to extract\nfeatures from the history data and Regressive Support Vector Machine (SVR)\ntrained with the features to predict the electricity consumption. The\nexperimental results show that the proposed RCNN-SVR model achieves higher\naccuracy than using the traditional RNN or SVM alone. The MSE, MAPE, and\nCV-RMSE of RCNN-SVR model are 0.8564, 1.975%, and 0.0687% respectively, which\nillustrates the low predicting error rate of the proposed model.","url_abs":"http://arxiv.org/abs/1810.08878v2","url_pdf":"http://arxiv.org/pdf/1810.08878v2.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":"a-regressive-convolution-neural-network-and","repo_url":"https://github.com/heaventian93/A-Regressive-Convolution-Neural-Network-and-Support-Vector-Regression-Model-for-Electricity-Consumpt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}