1. A neural network based several-hour- ahead electric load forecasting using similar days approach Paras Mandal, Tomonobu Senjyu, Naomitsu Urasaki, Toshihisa Funabashi Electrical Power and Energy Systems 28 (2006)

2. Outline  Introduction  Proposed method  Similar day selection  Artificial neural network(ANN)  Simulation and discussion  Conclusion

3. Introduction  Short-term load forecast is aimed at predicting system load over a short time interval in a wide range of time leads from minutes to several days.  Use of artificial neural networks (ANNs) to model load has become a trend. The main reason lies in its ability to learn complex and non-linear relationships.  Recently, several methods based on similarity have been reported for the purpose of load forecasting. The load curve is forecasted by using information of the days being similar to weather condition of the forecast day.

4. Proposed method  This paper presents an ANN-based several-hour-ahead (one-to-six-hour-ahead) load curve forecasting using similar days approach considering the temperature as the major climate factor. The output obtained from neural network is the corrected forecasted load of similar days data.

5. Similar Day Selection  Similar days corresponding to forecast day based on Euclidean norm in which weighted factors are used to evaluate the similarity between the forecast day and searched previous days.  Weighted factor is determined by using least square method based on the regression model that is constructed using historical temperature and load data

6. Similar Day Selection  Similar days are based on the same season.  The limits on the selection of similar days are past 65 days from the day before a forecast day, and past 65 days before and after the forecast day in the previous year. If the forecast day is changed, similar days are selected in the same manner.

7. Similar Day selection procedure  Step 1 ： Select similar days using Eq.(1).  Step 2 ： Load data of similar days at time t+1 are selected in Step 1 and assumed to be forecasted load L t +1 at time t+1.  Step 3 The value of the forecasted load that are obtained in Step 2 are assumed as actual load. Then, similar days at t+2 are selected by using Eq. (1) and Step 2 is repeated.  Step 4 Similarly, the forecasted load is obtained at time t+h- 1. Then, it is assumed to be actual load and selected similar days at time t+h.

8. Proposed neural network  Historical data show a short-term correlation between the total power demand and climatic information such as temperature, cloudiness, and wind, and sociological factors such as the day of week.  Because ANNs can encode complex, non-linear relations, researchers have used them to capture the relationships between the load and selected factors.

9. Proposed neural network  Proposed neural network

10. Proposed neural network  The neural network model is trained by using the data of past 45 days from the day before a forecast day, and past 45 days before and after the forecast day in the previous year. The training of neural network continues unless and until the error becomes constant.  If the forecast time is changed, neural network is retrained to obtain the relationships between load and temperature around the forecast day.

11. Simulation results  The performance of the developed method for short-term load forecasting has been tested using the actual hourly load and temperature data (for the year 1999–2000) of Okinawa Electric Power Company, Japan.  Load forecasting is done for the year 2000. To verify the predictive ability of the proposed method, we performed simulations for six cases (one-to- six-hour-ahead).

12. Simulation results  MAPE corresponding to months for all the cases in the year 2000

13. Simulation results  MAPE values for one-to-six-hour-ahead forecasting in different seasons  one-to-six-hour-ahead forecast error gradually deteriorates because the error of a succeeding hour contains the error of the previous hour.

14. Simulation results  One-hour-ahead load forecasting results in summer season for the period July 1, Saturday–July 7, Friday, 2000.

15. Simulation results  In the month of July, the temperature reaches maximum with high amount of humidity, causing increase in power demand

16. Simulation results  Two-hour-ahead load forecasting results in Autumn season for the period October 1, Sunday–October 7, Saturday, 2000.  Power demand during the weekend is lower than that of weekdays

17. Simulation results  Three-hour-ahead load forecasting for the period July 1, Saturday–July 7, Friday,2000  MAPE for three-hour-ahead is higher than that obtained for one-hour-ahead(0.80%)

18. Simulation results  Four-hour-ahead load forecasting for the period January 1, Saturday–January 7, Friday, 2000  Power demand is found to be reduced during Winter season when compared with other seasons

19. Conclusion  The proposed approach has a significant benefit. The algorithm is simple. The key equation in this algorithm is the Euclidean norm equation. All of these are easily programmed in a computer.  This paper presented a practical method for short-term load forecasting. The method is based on artificial neural network (ANN) combined similar days approach, which achieved a good performance in the very special region.