2019/07/31 by Ming Dong, Jian Shi, Qingxin Shi +1 · 15 citations
Computer Science · Decision Sciences · Engineering · Mathematics · #Artificial intelligence #Component (thermodynamics) #Computer science #Data mining #Energy Load and Power Forecasting #Engineering #Grey System Theory Applications #Machine learning #Mathematics #Mean squared error #Sequence (biology) #Statistics #Term (time) #Traffic Prediction and Management Techniques #cs.LG #cs.SY #eess.SY
paper · pdf · doi:10.1016/j.energy.2020.118209
published in Energy 206, 118209 (Elsevier BV) · 22 pages, 9 figures
openalex publication_date 2020/06/30 · arxiv created 2020/07/01 · arxiv updated 2020/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Long-term load forecast (LTLF) for area distribution feeders is one of the most critical tasks frequently performed in electric distribution utility companies. For a specific planning area, cost-effective system upgrades can only be planned out based on accurate feeder LTLF results. In our previous research, we established a unique sequence prediction method which has the tremendous advantage of combining area top-down, feeder bottom-up and multi-year historical data all together for forecast and achieved a superior performance over various traditional methods by real-world tests. However, the previous method only focused on the forecast of the next one-year. In our current work, we significantly improved this method: the forecast can now be extended to a multi-year forecast window in the future; unsupervised learning techniques are used to group feeders by their load composition features to improve accuracy; we also propose a novel selective sequence learning mechanism which uses Gated Recurrent Unit network to not only learn how to predict sequence values but also learn to select the best-performing sequential configuration for each individual feeder. The proposed method was tested on an actual urban distribution system in West Canada. It was compared with traditional methods and our previous sequence prediction method. It demonstrates the best forecasting performance as well as the possibility of using sequence prediction models for multi-year component-level load forecast.