{"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/using-solar-and-load-predictions-in-battery","title":"Using solar and load predictions in battery scheduling at the residential level","arxiv_id":"1810.11178","date":"2018-10-26","proceeding":null,"authors":["Richard Bean","Hina Khan"],"abstract":"Smart solar inverters can be used to store, monitor and manage a home's solar\nenergy. We describe a smart solar inverter system with battery which can either\noperate in an automatic mode or receive commands over a network to charge and\ndischarge at a given rate. In order to make battery storage financially viable\nand advantageous to the consumers, effective battery scheduling algorithms can\nbe employed. Particularly, when time-of-use tariffs are in effect in the region\nof the inverter, it is possible in some cases to schedule the battery to save\nmoney for the individual customer, compared to the \"automatic\" mode. Hence,\nthis paper presents and evaluates the performance of a novel battery scheduling\nalgorithm for residential consumers of solar energy. The proposed battery\nscheduling algorithm optimizes the cost of electricity over next 24 hours for\nresidential consumers. The cost minimization is realized by controlling the\ncharging/discharging of battery storage system based on the predictions for\nload and solar power generation values. The scheduling problem is formulated as\na linear programming problem. We performed computer simulations over 83\ninverters using several months of hourly load and PV data. The simulation\nresults indicate that key factors affecting the viability of optimization are\nthe tariffs and the PV to Load ratio at each inverter. Depending on the tariff,\nsavings of between 1% and 10% can be expected over the automatic approach. The\nprediction approach used in this paper is also shown to out-perform basic\n\"persistence\" forecasting approaches. We have also examined the approaches for\nimproving the prediction accuracy and optimization effectiveness.","url_abs":"http://arxiv.org/abs/1810.11178v1","url_pdf":"http://arxiv.org/pdf/1810.11178v1.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":"using-solar-and-load-predictions-in-battery","repo_url":"https://github.com/RichardBean/IEEE-Predict-Optimize-Challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"scheduling","task_name":"Scheduling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}