Browse > Article
http://dx.doi.org/10.7465/jkdi.2016.27.6.1601

Evaluation of weather information for electricity demand forecasting  

Shin, YiRe (WISE institute, Hankuk University of Foreign Studies)
Yoon, Sanghoo (Department of Computer Science and Statistics, Daegu University)
Publication Information
Journal of the Korean Data and Information Science Society / v.27, no.6, 2016 , pp. 1601-1607 More about this Journal
Abstract
Recently, weather information has been increasingly used in various area. This study presents the necessity of hourly weather information for electricity demand forecasting through correlation analysis and multivariate regression model. Hourly weather data were collected by Meteorological Administration. Using electricity demand data, we considered TBATS exponential smoothing model with a sliding window method in order to forecast electricity demand. In this paper, we have shown that the incorporation of weather infromation into electrocity demand models can significantly enhance a forecasting capability.
Keywords
Bias correction; forecasted electricity demand error; TBATS;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Box, G. E. P. and Cox, D. R. (1964). An analysis of transformation. Journal of the Royal Statistical Society B, 26, 211-252.
2 Cha, J., Lee, D., Kim, H. and Joo, S. K. (2015). The relationship between daily peak load and weather conditions using stepwise multiple regression. The proceedings of Korean Institute of Electrical Engineers, 475-476.
3 De Livera, A. M., Hyndman, R. J. and Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106, 1513-1527.   DOI
4 Cui, H. and Peng, X. (2015). Short-term city electric load forecasting with considering temperature effects : An improved ARIMAX model. Mathematical Problems in Engineering, Available from http://dx.doi.org/10.1155/2015/589374.
5 Lee, Y. S., Kim, J., Jang, M. S. and Kim, H. G. (2013). A study on comparing short-term wind power prediction models in Gunsan wind farm. Journal of the Korean Data & Information Science Society, 24, 585-592.   DOI
6 Lim, J. H., Kim, S. Y., Park, J. D. and Song, K. B. (2013). Representative temperature assessment for improvement of short-term load forecasting cccuracy. Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, 27, 39-43.
7 Kim, C. H. (2013a). Electricity demand patterns analysis by daily and timely time series. Korea Development Institute, 13-03, Sejong, Korea.
8 Ramanathan, R., Engle, R., Granger, C. W., Vahid-Araghi, F. and Brace, C. (1997). Short-run forecasts of electricity loads and peaks. International Journal of Forecasting, 13, 161-174.   DOI
9 Kim, C. H. (2013b). Short-term electricity demand forecasting using complex seasonal exponential smoothing. Korea Development Institute, 13-06, Sejong, Korea.
10 Kim, C. H. (2014). Electricity demand forecasting using mixed data sampling model. Korea Development Institute, 13-06, Sejong, Korea.
11 Shin, D. and Jo, H. (2014). A empirical study on the climate factor sensitivity and threshold temperature of daily maximum electricity consumption in Korea. Korea Economic and Business Association, 32, 175-212.
12 Yoon, S. and Choi, Y. (2015). Functional clustering for electricity demand data: A case study. Journal of the Korean Data & Information Science Society, 26, 885-894.   DOI
13 Shin, Y. and Yoon, S. (2016). Electricity forecasting model using specific time zone. Journal of the Korean Data & Information Science Society, 27, 275-284.   DOI
14 Taylor, J. W. and Buizza, R. (2003). Using weather ensemble predictions in electricity demand forecasting. International Journal of Forecasting, 19, 57-70.   DOI
15 Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204, 139-152.   DOI