1. Short-term Load Forecasting Using Improved Similar Days Method Qingqing Mu, Yonggang Wu, Xiaoqiang Pan, Liangyi Huang, Xian Li Power and Energy Engineering Conference (APPEEC), 2010

2. Outline  Introduction  Forecasting methods  Proposed method  Index-mapping database  Evaluate similarity  Prediction algorithm  Improved similar-day method  Experiment result  Conclusion

3. Introduction  Load forecasting can be divided into three categories:  short-term forecasts: an hour to a week  Medium-term forecasts: a week to a year  long-term forecasts: longer than a year.  Short-term load forecasting can help to estimate load flows, make decisions that can prevent overloading, improve network reliability and to reduce occurrences of equipment failures and blackouts.  Energy price contract evaluation on energy market

4. Forecasting methods  Methods on forecasting  Multiplicative autoregressive model  linear model  Non-linear model  Kalman filtering  Nonparametric regression  Most popular methods are linear regression models and decompose the load into basic and weather dependent components

5. Proposed method  Many factors influencing the daily load of power system, such as weather condition, temperature, day type and so on.  An index-mapping database is designed for each factor to obtain mapping value. Similarity of day characteristics is introduced to evaluate the similarity between the historical day and the forecasting day.  h similar days are selected to forecast the load.

6. Flow chart historical days characteristic s Index-mapping database Forecasting day characteristics Evaluate similarity & select h similar days Prediction algorithm Index-mapping database historical days load Forecasting day load

7. Index-mapping database  Characteristics  date type : ordinary day & holiday  weather situation : rainstorms  week type : Mon, Tue…  temperature

8. Similarity of different days

9. Prediction algorithm

10. Improved similar-day method  Similarity weighting  There is no obvious distinction between most similar and less similar days. Modified formula (n is set to be 110 by experiment)  Select h similar days  Only high similarity days are taken, setting up a threshold  #Similarity higher than 0.6 > h, most similar h days are kept  #Similarity higher than 0.6 < h, only similarity higher than 0.6 are kept

11. Improved similar-day method  Treatment when no similar days  If no similarity of historical day is higher than the threshold, n days before these n historical day are used for forecasting, and the n days before forecasting day are abandoned.  Usually happened when suddenly weather changed.

12. Experiment result  Use load and weather data of a week in June 2008 in Hainan  The historical days selected is 29 days (n=29)

13. Experiment result

14. Conclusion  In this paper, increasing the weight of the most similar days, the forecasting error decreases greatly. And we made a discussion on how to select similar days and situation without similar days.  At the same time, some adjustment on certain characteristics must be made in time according to weather variance and the change of some dominant factors.  Similar days method can also combined with other methods like gray theory for load forecasting, and the result would be better.