2017/02/26 by The-Hien Dang-Ha, Dang-Ha, The-Hien, Filippo Maria Bianchi +3 · 1 citation
Computer Science · Decision Sciences · Engineering · #Energy Load and Power Forecasting #FOS: Mathematics #Image and Signal Denoising Methods #Optimization and Control (math.OC) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.1702.08025
openalex publication_date 2017/02/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods.