2020/12/22 by Ming Dong, Dong, Ming
Computer Science · Engineering · #Computer science #Energy Load and Power Forecasting #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Industrial engineering #Machine Learning (cs.LG) #Optimal Power Flow Distribution #Power (physics) #Power System Reliability and Maintenance #Process (computing) #Profiling (computer programming) #Reliability (semiconductor) #Reliability engineering #Systems and Control (eess.SY) #Term (time) #cs.LG #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2012.13344
published in arXiv (Cornell University) (Cornell University) · 3 pages
openalex publication_date 2020/12/22 · arxiv created 2021/11/03 · arxiv updated 2021/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Today, the adoption of new technologies has increased power system dynamics significantly. Traditional long-term planning studies that most utility companies perform based on discrete power levels such as peak or average values cannot reflect system dynamics and often fail to accurately predict system reliability deficiencies. As a result, long-term future continuous profiles such as the 8760 hourly profiles are required to enable time-series based long-term planning studies. However, unlike short-term profiles used for operation studies, generating long-term continuous profiles that can reflect both historical time-varying characteristics and future expected power magnitude is very challenging. Current methods such as average profiling have major drawbacks. To solve this challenge, this paper proposes a completely novel approach to generate such profiles for multiple generation types. A multi-level profile synthesis process is proposed to capture time-varying characteristics at different time levels. The proposed approach was evaluated based on a public dataset and demonstrated great performance and application value for generating long-term continuous multi-type generation profiles.