Short-Term Load Forecasting Using System-Type Neural Network Architecture

1. Short-Term Load Forecasting Using System-Type Neural Network Architecture Shu Du, Graduate Student Mentor: Kwang Y. Lee, Professor and Chair Department of Electrical and Computer Engineering Baylor University

2. Outline • Introduction and Background • Objectives • Load Forecasting Categories • Load Forecasting Methods • Proposed Approach • Regression and Rearrangement • System-Type Neural Network Method • Learning Algorithm of System-Type Neural Network • Extrapolation and Interpolation • Simulation Results • Rearrangement • Output of Semigroup Channel • Extrapolation • Conclusions

3. Introduction and Background • Objective • Electric power generation, transmission, distribution, security • Increase or decrease output of generators • Interchange power with neighboring systems • Prevent overloading and reduce occurrences of equipment failures • Electric power market • Price settings • Schedule spinning reserve allocation properly

4. Introduction and Background • Load Forecasting Categories • Short-term load forecasting • One hour ~ One week • Control and schedule power system in everyday operations • Medium-term and Long-term load forecasting • One week ~ longer than one year • Determine capacity of generation, transmission, distribution systems, type of facilities required in transmission expansion planning, development of power system infrastructure, etc.

5. Introduction and Background • Load Forecasting Methods • Parametric methods • Regression method • Time series Autoregressive Moving Average (ARMA) Spectral expansion technique (Fourier Series) State equations • Artificial intelligence methods • Artificial neural networks Feedforward network Recurrent network • Fuzzy logic • Expert systems

6. Proposed Approach • Regression and Rearrangement • Regression • Objective Represent given load with respect to two major variables—time and temperature • Load Form -----Base load component (time factor) -----Weather sensitive load component (weather factor) -----Load component (other factors)

7. Day Temperature Rearrangement Hour Hour 1 2 24 1 2 24 Proposed Approach • Regression and Rearrangement • Rearrangement • Objective Minimize the fluctuation caused by hourly temperature Obtain the smoothness of the given load data • Implementation Align given load based upon magnitudes of hourly temperatures Load before Rearrangement Load after Rearrangement

8. Proposed Approach • System-Type Neural Network Method • Algebraic Decomposition • Objective Form an approximation load data to • Implementation • Reorganize given load into a parameterized set • Select elements and orthonormalize them to a basis set by Gram-Schmidt process • Determine the linear combination of basis set for each element • Combine the coefficient vector and the basis set to achieve an approximation

9. Function Channel (NN1) Semigroup Channel (NN2) Proposed Approach • System-Type Neural Network Method • Function Channel • Structure— RBF networks • Each network implements one of orthonormal basis functions • Semigroup Channel • Structure—Simple Recurrent Network • Smoothen the coefficient vector and Realize semigroup property

10. Proposed Approach • Learning Algorithm of System-Type Neural Network • Function Channel • RBF network can be designed rather than trained • RBF networks emulate selected basis functions • Semigroup Channel • Primary Objective – Replicate and smoothen the vector with a vector which has the semigroup property • Secondary Objective – Acquire a semigroup property in the weight space which is the basis for extrapolation • The entire trajectory is sliced into a nested sequence of trajectories

11. Temperature Temperature Extrapolated Coefficient Interpolated Coefficient 4 4 Decompose & Smoothen Decompose & Smoothen 3 3 2 2 1 1 Hour Hour 4 3 4 5 1 1 2 2 24 24 Proposed Approach • Extrapolation and Interpolation • Extrapolation • Extrapolation is needed only when temperature forecast at a given hour exceeds the historical bounds at the same time • Interpolation • Interpolation is needed when temperature forecast at a given hour falls into the historical temperature range at the same time Load after Rearrangement Load after Rearrangement Extrapolation of Coefficient Interpolation of Coefficient

12. Simulation Results • Forecasting Procedure • Data Source • New England Independent System Operator • Historical Data • Load – load for the year 2002 • Temperature – weighted average hourly temperature of 8 stations in the New England area • Pattern • Weekday pattern (Mon ~ Fri) and Weekend pattern (Sat, Sun) • Next Day Forecasting • Previous loads and temperatures in the length of four weeks

13. Simulation Results • Simulation of Forecasting A Weekday Load • Rearrangement Rearrange

14. Simulation Results • Simulation of Forecasting A Weekday Load • Output of Semigroup Channel

15. Simulation Results • Simulation of Forecasting A Weekday Load • Extrapolation

16. Simulation Results • Regression Load Forecasting Results

17. Conclusions • Next Day Load Forecasting based upon Weather Forecast • A mathematical approach referred to as algebraic decomposition is investigated • The system-type neural network architecture combining Radial Basis Function Networks and a Simple Recurrent Network is proposed • A new training algorithm in the SRN is proposed • Regression and Rearrangement are performed to guarantee smoothness of coefficient vector • Interpolation and Extrapolation are implemented based on temperatures • Much better results with respect to actual load and removal of regression are expected if load and temperature are highly correlated to each other

18. THANK YOU