Hybrid Load Forecasting Method With Analysis of Temperature Sensitivities

1. Hybrid Load Forecasting Method With Analysisof Temperature Sensitivities Authors: Kyung-Bin Song, Seong-Kwan Ha, Jung-Wook Park, Dong-Jin Kweon, Kyu-Ho Kim From: IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 21, NO. 2, MAY 2006 報告人: Chao-Dian Chen date:2008.6.13

2. INTRODUCTION • Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. • The electric power load forecasting problem is not easy to handle due to its nonlinear and random-like behaviors of system loads, weather conditions, and variations of social and economic environments, etc.

3. Techniques of load forecasting • Multiple linear regression, • stochastic time series, • Kalman filter, • expert system, • fuzzy systems, • artificial neural networks

4. Many studies have been reported to improve the accuracy of load forecasting using the conventional methods such as regression- based method [1], Kalman filter [2], and knowledge-based expert system [3]. However, these techniques have a possibility to lack the accuracy of prediction with the higher load forecasting errors in some particular time zones, which are, for example, the weekdays of the summer season, weekend, and/or Monday.

5. In this paper, the new hybrid load forecasting algorithm, which consists of the fuzzy linear regression method and the general exponential smoothing algorithm with the analysis of temperature sensitivities, is proposed for the accurate forecast of 24-h daily loads. • According to the lifestyle of consumers, the load patterns of weekends and Monday are so different, compared to those of weekdays.

7. [A] Load Forecasting for Tuesday Through Friday In this case, the actions performed in the flow for the 24-h daily load forecasting are as follows. • Step 1) If the predicted day is belonging to a summer season, go to step 4). Otherwise, it is the day on spring, fall, or winter seasons. • Step 2) Construct input information using the load data during three days (which are subject to Monday through Friday) before the predicted day. • Step 3) Forecast the maximum load using the exponential smoothing method. • Step 4) In case that the predicted day belongs to a summer season, the temperature sensitivities are computed using the variations of the load and temperature between the predicted day and its one previous day. • Step 5) Forecast the maximum load with the temperature sensitivities calculated in step 4).

8. [B] Load Forecasting for Saturday, Sunday, and Monday The steps taken for the load forecasting of weekends and Monday (the flow of Fig. 1) are given in the following. • Step 1) As mentioned before, the load patterns of weekends and Monday are different depending on the lifestyle of consumers, compared to those of weekdays. Therefore, its characteristics need to be observed carefully. • Step 2) Construct fuzzy input data using the load difference between data during the previous three weekdays from the predicted day and data in the same date as the predicted day. • Step 3) Forecast the maximum and the minimum loads using the fuzzy linear regression method. The use of the minimum load makes it possible to predict the more precisely by providing the normalized values, in which are 1 at the maximum load and 0 at the minimum load.

9. Exponential Smoothing Method • The general exponential smoothing method is developed to provide the more reliable performance for the load forecasting of weekdays without regard to weather conditions and special variations. In other words, the exponential smoothing can be applied to predict the only daily loads of weekdays during spring, fall, and winter, except for a summer season, when the load patterns are strongly affected by the temperature.

10. A smoothing time-series model is as follows: • is the forecasted value, is the real value, is the day of load forecasting, and N is the number of the observed data.

11. the forecasted value can be expressed as we have

12. the general exponential smoothing model is reformulated as

15. Fuzzy Linear Regression Method • Linear regression is a statistical method to model the relationship between two variables by fitting a linear equation to observed data. The fuzzy linear regression method can be used for forecasting under the assumption of the continuing correlation between the variables in the future by fuzzy numbers.

16. An associated equation for the linear regression model is

20. 參考文獻 • Y. Yoon et al., “Development of the Integrated System for Power System Operational Planning and Analysis,” KEPRI, Tech. Rep. TR.94YJ 15.J1998.89, Dec. 1998. • D. H. Hong et al., “Fuzzy linear regression analysis for fuzzy input output data using shape preserving operations,”Fuzzy Sets Syst., vol. 122, pp. 513–526, Sep. 2001

23. TEMPERATURE SENSITIVITIES • The change of temperature during spring, fall, and winter seasons is small. Therefore, its effect on the load patterns can be passed over. However, during a summer season, the electrical load demand is significantly increased due to many uses of the air conditioners resulting from the high temperature. • The relationships between the load and temperature in May, July, August, and October 2000 in Korea are shown in Figs. 3–6, respectively. These data show that the peak load patterns are similar to those of temperature with the high correlations for July and August (in summer). However, there is no relationship between the peak load and temperature for May (in spring) and October (in fall). In this section, the accuracy of load forecasting during a summer season is increased by the analysis of temperature sensitivities, which is described in the following.

29. Conclusion • First, theloads in weekdays are forecasted using the general exponentialsmoothing method. Especially, the analysis of temperaturesensitivities was carried out to improve the performance ofload forecasting during a summer season. Second, the fuzzylinear regression method was applied to the load forecasting inweekends and Monday.