Hybrid Load Forecasting Method With Analysis of Temperature Sensitivities Kyung-Bin Song, Seong-Kwan Ha, Jung-Wook Park, Dong-Jin Kweon, Kyu-Ho Kim IEEE.

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Presentation transcript:

Hybrid Load Forecasting Method With Analysis of Temperature Sensitivities Kyung-Bin Song, Seong-Kwan Ha, Jung-Wook Park, Dong-Jin Kweon, Kyu-Ho Kim IEEE transactions on POWER SYSTEMS, May 2006

Outline  Introduction  Hybrid Short-Term Load Forecasting (STLF)  Methods  Exponential Smoothing  Fuzzy Linear Regression  Temperature Sensitivities  Results  Conclusion

Introduction  Conventional methods on STLF are not suitable for higher load forecasting errors in some particular time zones, like weekends. So intelligence techniques like fuzzy and neural network are recently as an alternative in forecasting.  In this paper, a new hybrid load forecasting consist of fuzzy linear regression and the general exponential smoothing with analysis of temperature sensitivities is proposed. Each has their usage on different situation.

Hybrid Short-Term Load Forecasting  General patterns of weekdays are almost identical, but on weekends and Mondays are depend on consumers lifestyle. Especially, the load patterns of a summer season are strongly affected by the temperature.  In this paper, Mondays and weekends are processed together for better accuracy. And hybrid STLF with temperature is used in summer.

Flow chart

Methods – Exponential Smoothing

Normalized

Method - Fuzzy linear regression

Method – temperature sensitivities

Results  The algorithm is applied to forecast a week(different seasons) of 1996  Comparative algorithm is the “top-down developed method”, called Load Forecasting Engineering System(LOFES), by Korea Electric Power Research Institute(KEPRI), which use time-domain regressive analysis with past load profile and weather data.

Results  March 26 to April 1(spring)

Results  October 29 to November 4(fall)

Results  July 9 to July 15

Conclusion  This paper proposed a new hybrid short-term load forecasting algorithm. It combines exponential smoothing, fuzzy linear regression and temperature sensitivities.  The temperature sensitivity is a new approach and it actually improves the load forecast accuracy in summer