| Title |
Day-Ahead load Forecasting Algorithm in Spring Using Daily Temperature Sensitivity and Weights in Exponential Smoothing |
| Authors |
Seung-Min Cho ; Kyeong-Hwan Kim ; Tae-Geun Kim ; Sung-Guk Yoon ; Kyung-Bin Song |
| DOI |
http://doi.org/10.5207/JIEIE.2025.39.2.128 |
| Keywords |
Day-ahead load forecasting; Exponential smoothing; Short-term load forecasting; Temperature sensitivity |
| Abstract |
This study proposes short-term load forecasting algorithm using an improved exponential smoothing method to improve the accuracy of day-ahead load forecasting in spring. The proposed algorithm defines the range of temperature insensitivity in spring and calculates daily sensitivity of hourly temperature. Daily exponential smoothing coefficients are optimized to minimize forecasting errors. Case studies were performed on the proposed algorithm to calculate the forecast error of day-ahead load forecasting in the spring of 2022 and 2023. The proposed algorithm showed an improvement of the average prediction accuracy in spring in by 12.85%p for two years compared to the exponential smoothing and LSTM algorithm errors of the short-term load forecasting S/W of the Korea Power Exchange. |