1. Dr. Yukun Bao School of Management, HUST Business Forecasting: Experiments and Case Studies

2. Dr. Yukun Bao School of Management, HUST Case 3: Load Forecasting

3. June 10, 2015 Business Forecasting: Experiments and Case Studies 3 Contents 1. Problem Statement 2. Modeling tasks 3. Data Analysis 4. Experimental Results 5. Summary

4. June 10, 20154 1. Problem Statement Business Forecasting: Experiments and Case Studies

5. June 10, 20155 1. Problem Statement Load Forecasting Predict the future electric demand based on historical load, climate factors, seasonal factors, social activities, and other possible factors. Typical applications Short-term: from one hour to one week ahead forecasts Medium-term: a week to a year ahead Long-term: Longer than a year Forecasts for different time horizons are important for different operations within a utility company Business Forecasting: Experiments and Case Studies

6. June 10, 20156 1. Problem Statement Benefits of accurate forecasting of Load demand Utilities/ System Operators/Generators/ Power Marketers/ other participants in electric generation, transmission, distribution, and markets automatic generation control, safe and reliable operation, and resource dispatch Energy transaction in deregulated and competitive electricity markets infrastructure development … Business Forecasting: Experiments and Case Studies

7. June 10, 20157 1. Problem Statement Goal of this case study Primary experimental study in day-ahead load forecast (Short-term Load forecasting) Data Hourly load and temperature data from North-American electric utility Forecasting Methods ( by Matlab/R) Support Vector Regression Artificial Neural Network ARIMA ES MA Business Forecasting: Experiments and Case Studies

8. June 10, 20158 2. Modeling Tasks Step1: Data Analysis (SPSS/Matlab) Preprocess Visualize and Analysis Step2: Constructing Model Input features selection Parameters Optimization Step3: Experimental Results and Analysis Run Model Results and comparison Business Forecasting: Experiments and Case Studies

9. June 10, 20159 3. Data Analysis (1) Testing period: January in 1991 Training period: The previous three months hourly data Preprocess: Zero values [0,1] Business Forecasting: Experiments and Case Studies

10. June 10, 201510 3. Data Analysis (1)-Descriptive SPSS: Business Forecasting: Experiments and Case Studies Descriptive Statistics NRange Minimu m Maximu mMean Std. DeviationVarianceSkewnessKurtosis Statisti c Std. ErrorStatistic Std. Error Load29043285.001350.004635.002623.7999616.25958 379775.8 76.180.045-.417.091 Temperature290454.0012.0066.0042.94909.3355387.152-.854.045.928.091 Valid N (listwise) 2904

11. June 10, 201511 3. Data Analysis (1)-ScatterPlot In SPSS: Graphs  Legacy Dialogs  Scatter/Dot…  Simple Scatter Business Forecasting: Experiments and Case Studies

12. June 10, 201512 3. Data Analysis (2) Hourly load from 01, May,1990 --- 05, July,1990 load demands have multiple seasonal patterns including the daily and weekly periodicity. load level in the weekend days and holidays is lower than that in working days Business Forecasting: Experiments and Case Studies Fig.3 Hourly load from 01, May,1990 to 05, July,1990

13. June 10, 201513 3. Data Analysis (3) Average hourly load during 24 hours varies from hour to hour working days except Friday have similar shapes and similar magnitude weekend days < working days Business Forecasting: Experiments and Case Studies Fig.4 Hourly load during a day

14. June 10, 201514 3. Data Analysis (4) Temperature v.s. Load Demand nonlinear relationship Business Forecasting: Experiments and Case Studies Fig.5 Correlation between the load and temperature.

15. June 10, 201515 3. Data Analysis (4) Temperature v.s. Load Demand Only for training and testing period Business Forecasting: Experiments and Case Studies Fig.5 Correlation between the load and temperature. Correlations LoadTemperature LoadPearson Correlation1-.574 ** Sig. (2-tailed).000 N2904 TemperaturePearson Correlation-.574 ** 1 Sig. (2-tailed).000 N2904 **. Correlation is significant at the 0.01 level (2-tailed).

16. June 10, 201516 3. Data Analysis (5) Input features for SVR/ANN hourly load values of the previous 12 hours, and similar hours in the previous one week Temperature variables for time point that the load was included, plus the forecasted temperature for the forecasting hour. daily and hourly calendar indicators Business Forecasting: Experiments and Case Studies

17. June 10, 201517 4. Experiments Forecasting Methods ( by Matlab/R) Support Vector Regression Artificial Neural Network ARIMA ES MA Input features: all the above features Parameter optimization: Grid search, PSO Business Forecasting: Experiments and Case Studies

18. June 10, 201518 4. Experiments Evaluation measures Business Forecasting: Experiments and Case Studies MetricsFormula

19. June 10, 201519 4. Experiments Results Business Forecasting: Experiments and Case Studies MAPE(%)MASEDS(%) SVR_GS6.950.7789.23 SVR_PSO7.010.7990.19 NN8.550.8685.15 ARIMA9.240.9576.91 ES10.111.79261.24 MA13.622.4245.09

20. June 10, 201520 4. Experiments Results Business Forecasting: Experiments and Case Studies

21. June 10, 201521 Summary Electricity load forecasting is an important issue to operate the power system reliably and economically. In this case study, support vector regression (SVR) is applied for short-term load forecasting. Characteristics of the hourly loads are firstly analyzed to select the input features. Then forecasting results of SVR with two parameter optimization methods are compared with several benchmark forecasting models. Further topics: features selection method, separated modeling for each day and special days. Business Forecasting: Experiments and Case Studies