Dr. Yukun Bao School of Management, HUST

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

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

3. Contents • Problem Statement • Modeling tasks • Data Analysis • Experimental Results • Summary Business Forecasting: Experiments and Case Studies

4. 1. Problem Statement Business Forecasting: Experiments and Case Studies

5. 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. 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. 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. 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. 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. 3. Data Analysis (1)-Descriptive • SPSS: Business Forecasting: Experiments and Case Studies

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

12. 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 Fig.3 Hourly load from 01, May,1990 to 05, July,1990 Business Forecasting: Experiments and Case Studies

13. 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 Fig.4 Hourly load during a day Business Forecasting: Experiments and Case Studies

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

15. 3. Data Analysis (4) • Temperature v.s. Load Demand • Only for training and testing period Fig.5 Correlation between the load and temperature. Business Forecasting: Experiments and Case Studies

16. 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. 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. 4. Experiments • Evaluation measures Business Forecasting: Experiments and Case Studies

19. 4. Experiments • Results Business Forecasting: Experiments and Case Studies

20. 4. Experiments • Results Business Forecasting: Experiments and Case Studies

21. 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