1. SHORT TERM LOAD FORECASTING USING NEURAL NETWORKS AND FUZZY LOGIC George G Karady Arizona State University Short-term load forecasting

2. Short Term Load ForecastingContent • Overview of Short term load forecasting • Introduction • Definitions and expected results • Importance of Short-term load forecasting • Impute data and system parameters required for load forecasting • Concept • Major load forecasting techniques • Concept of STLF model Development • Statistical methods • (Multiply liner regression, • stochastic time series e.t.c) Short-term load forecasting

3. Short Term Load ForecastingContent • Artificial Neural Networks • Building block: a feed forward network • Load forecasting engine • Results • Numerical example • Fuzzy logic and evolutionary programming Short-term load forecasting

4. Overview of Short Term Load Forecasting (STLF) Short-term load forecasting

5. Introduction • The electrical load increases about 3-7% per year for many years. • The long term load increase depends on the population growth, local area development, industrial expansion e.t.c. • The short term load variation depends on weather, local events, type of day (Weekday or Holiday or Weekend) e.t.c. Short-term load forecasting

6. Introduction • The building of a power plant requires: • 10 years (Nuclear) • 6 years (Large coal-fired) • 3 years (combustion turbine) • The electric system planing needs the forecast of the load for several years. • Typically the long term forecast covers a period of 20 years Short-term load forecasting

7. Introduction • The planning of maintenance, scheduling of the fuel supply etc. calls for medium term load forecast . • The medium term load forecast covers a period of a few weeks. • It provides the peak load and the daily energy requirement Short-term load forecasting

8. Introduction • The number of generators in operation, the start up of a new unit depends on the load. • The day to day operation of the system requires accurate short term load forecasting. Short-term load forecasting

9. Introduction • Typically the short term load forecast covers a period of one week • The forecast calculates the estimated load for each hours of the day (MW). • The daily peak load. (MW) • The daily or weekly energy generation. (MWh) Short-term load forecasting

10. Introduction • The utilities use three types of load forecasting: • Long term (e.g. 20 years) • Medium term. (e.g. 3-8 weeks) • Short term (e.g. one week) • This lecture presents the short term load forecasting techniques Short-term load forecasting

11. Definitions and Expected Results(Big picture) • The short term load forecasting provides load data for each hour and cover a period of one week. • The load data are: • hourly or half-hourly peak load in kW • hourly or half-hourly values of system energy in kWh • Daily and weekly system energy in kWh Short-term load forecasting

12. Definitions and Expected Results(Big picture) • The short term load forecasting is performed daily or weekly. • The forecasted data are continuously updated. • Typical short-term, daily load forecast is presented in the Table in the next page. (Salt River Project, SRP) Short-term load forecasting

13. Definitions and Expected Results(Big picture) Typical short-term, daily load forecast from Salt River Project. Hourly Load in MW Short-term load forecasting

14. Definitions and Expected Results(Big picture) Typical short-term, daily load forecast from Salt River Project. Hourly Load in MW Short-term load forecasting

15. Importance of Short-term Load Forecasting • Provide load data to the dispatchers for economic and reliable operation of the local power system. • The timeliness and accuracy of the data affects the cost of operation. • Example: The increase of accuracy of the forecast by 1% reduced the operating cost by L 10M in the British Power system in 1985 Short-term load forecasting

16. Importance of Short-term Load Forecasting • The forecasted data are used for: • Unit commitment. • selection of generators in operation, • start up/shut down of generation to minimize operation cost • Hydro scheduling to optimize water release from reservoirs • Hydro-thermal coordination to determine the least cost operation mode (optimum mix) Short-term load forecasting

17. Importance of Short-term Load Forecasting • The forecasted data are used for: • Interchange scheduling and energy purchase. • Transmission line loading • Power system security assessment. • Load-flow • transient stability studies using different contingencies and the predicted loads. Short-term load forecasting

18. Importance of Short-term Load Forecasting • These off-line network studies: • detect conditions under which the system is vulnerable • permit preparation of corrective actions • load shedding, • power purchase, • starting up of peak units, • switching off interconnections, forming islands, • increase spinning and stand by reserve Short-term load forecasting

19. Impute Data and System Parameters for Load Forecasting • The system load is the sum of individual load. • The usage of electricity by individuals is unpredictable and varies randomly. • The system load has two components: • Base component • Randomly variable component Short-term load forecasting

20. Impute Data and System Parameters for Load Forecasting • The factors affecting the load are: • economical or environmental • time • weather • Unforeseeable random events Short-term load forecasting

21. Impute Data and System Parameters for Load Forecasting Economical or environmental factors • Service area demographics (rural, residential) • Industrial growth. • Emergence of new industry, change of farming • Penetration or saturation of appliance usage • Economical trends (recession or expansion) • Change of the price of electricity • Demand side load management Short-term load forecasting

22. Impute Data and System Parameters for Load Forecasting • The time constraints of economical or environmental factors are slow, measured in years. • This factors explains the regional variation of the load model (New York vs. Kansas) • The load model depends on these slow changing factors and has to be updated periodically Short-term load forecasting

23. Impute Data and System Parameters for Load Forecasting Time Factors affecting the load • Seasonal variation of load (summer, winter etc.). The load change is due to: • Change of number of daylight hours • Gradual change of average temperature • Start of school year, vacation • Calls for a different model for each season Short-term load forecasting

24. Impute Data and System Parameters for Load Forecasting Typical Seasonal Variation of Load Summer peaking utility Short-term load forecasting

25. Impute Data and System Parameters for Load Forecasting Time Factors affecting the load Daily variation of load. ( night, morning,etc) Short-term load forecasting

26. Impute Data and System Parameters for Load Forecasting Weekly Cyclic Variation • Saturday and Sunday significant load reduction • Monday and Friday slight load reduction • Typical weekly load pattern: Short-term load forecasting

27. Impute Data and System Parameters for Load Forecasting Time Factors affecting the load • Holidays (Christmas, New Years) • Significant reduction of load • Days proceeding or following the holidays also have a reduced load. • Pattern change due to the tendency of prolonging the vacation Short-term load forecasting

28. Impute Data and System Parameters for Load Forecasting • Weather factors affecting the load • The weather affects the load because of weather sensitive loads: • air-conditioning • house heating • irrigation Short-term load forecasting

29. Impute Data and System Parameters for Load Forecasting Weather factors affecting the load The most important parameters are: • Forecasted temperature • Forecasted maximum daily temperature • Past temperature Regional temperature in regions with diverse climate Short-term load forecasting

30. Impute Data and System Parameters for Load Forecasting Weather Factors Affecting the Load The most important parameters are: • Humidity • Thunderstorms • Wind speed • Rain, fog, snow • Cloud cover or sunshine Short-term load forecasting

31. Impute Data and System Parameters for Load Forecasting Random Disturbances Effects on Load • Start or stop of large loads (steel mill, factory, furnace) • Widespread strikes • Sporting events (football games) • Popular television shows • Shut-down of industrial facility Short-term load forecasting

32. Impute Data and System Parameters for Load Forecasting • The different load forecasting techniques use different sets of data listed before. • Two -three years of data is required for the validation and development of a new forecasting program. • The practical use of a forecasting program requires a moving time window of data Short-term load forecasting

33. Impute Data and System Parameters for Load Forecasting • The moving time window of data requires: • Data covering the last 3-6 weeks • Data forecasted for the forecasting period, generally one week Short-term load forecasting

34. Impute Data and System Parameters for Load Forecasting • The selection of long periods of historical data eliminates the seasonal variation • The selection of short periods of historical data eliminates the processes that are no longer operative. Short-term load forecasting

35. Impute Data and System Parameters for Load Forecasting • The forecasting is a continuous process. • The utility forecasts the load of its service area. • The forecaster • prepares a new forecast for everyday and • updates the existing forecast daily • The data base is a moving window of data Short-term load forecasting

36. Major Load Forecasting Techniques • Statistical methods • Artificial Neural Networks • Fuzzy logic • Evolutionary programming • Simulated Annealing and expert system • Combination of the above methods Short-term load forecasting

37. Major Load Forecasting Techniques • The statistical methods will be discussed briefly to explain the basic concept of the load forecasting • This lecture concentrates on load forecasting methods using neural networks and fuzzy logic Short-term load forecasting

38. Concept of STLF Model Development • Model selection • Calculation and update of model parameters • Testing the model performance • Update/modification of the model if the performance is not satisfactory Short-term load forecasting

39. Concept of STLF Model Development • Model selection • Selection of mathematical techniques that match with the local requirements • Calculation and update of model parameters • This includes the determination of the constants and • selection of the method to update the constants values as the circumstance varies. (seasonal changes) Short-term load forecasting

40. Concept of STLF Model Development • Testing the model performance • First the model performance has to be validated using 2-3 years of historical data • The final validation is the use of the model inreal life conditions. The evaluation terms are: • accuracy • ease of use • bad/anomalous data detection Short-term load forecasting

41. Concept of STLF Model Development • Update/modification of the model if the performance is not satisfactory • Due to the changing circumstances (regional gross, decline of local industry etc.) the model becomes obsolete and inaccurate, • Model performance, accuracy has to be evaluated continuously • Periodic update of parameters or the change of model structure is needed Short-term load forecasting

42. Artificial Neural Networksfor STLF Short-term load forecasting

43. Artificial Neural Networksfor STLF • Several Artificial Neural Network (ANN) based load forecasting programs have been developed. • The following neural networks were tested for load forecasting: • Feed-forward type ANN • Radial based ANN • Recurrent type ANN Short-term load forecasting

44. Building Blocks of a Feed Forward Network • A Feed forward Three-Layered Perceptron Type ANN was selected to demonstrate the short term load forecasting technique. • The selected network forecasts: • Hourly loads • Peak load of the day • Total load of the day. Short-term load forecasting

45. Building Blocks of a Feed Forward Network • The forecasting with neural network will be demonstrated using a feed forward three-layerednetwork to forecast the peak load of the day. • The network has: • one output: (load in kW) • three input: (previous day max. load and temperature, forecasted max. temperature)_ Short-term load forecasting

46. Building Blocks of a Feed Forward Network • The structure of the Feed Forward Three-Layered Perceptron Type ANN is presented on the next page. • The network contains: • i = 1.. 3 input layer nodes • j = 1…5 hidden layer nodes • k = 1 output layer nodes Short-term load forecasting

47. Artificial Neural Networksfor STLF Short-term load forecasting

48. Building Blocks of a Feed Forward Network • The inputs are: • X1 previous day max. load • X2 previous day max. temperature • X3 forecasted max. temperature • Wij weight factor between input and hidden layer • wj weight factor between hidden layer and output Short-term load forecasting

49. Building Blocks of a Feed Forward Network • A sigmoid function is placed in the nodes (neurons) of the hidden layer and output node. • The sigmoid equation for an arbitrary Z function is: • Youtput :maximum load Short-term load forecasting

50. Building Blocks of a Feed Forward Network • Inputs Xi are multiplied by the connection weights (Wij) and passed on to the neurons in the hidden layer. • The weighted inputs (Xi*Wij) to each neurons are added together and passed through a sigmoid function. • Input of hidden layer neuron 1 is: Short-term load forecasting