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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. Outline. Introduction and Background Objectives Load Forecasting Categories

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short term load forecasting using system type neural network architecture

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

outline
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
introduction and background
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
introduction and background4
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.
introduction and background5
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
proposed approach
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)

proposed approach7

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

proposed approach8
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
proposed approach9

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

proposed approach10
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
proposed approach11

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

simulation results
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
simulation results13
Simulation Results
  • Simulation of Forecasting A Weekday Load
    • Rearrangement

Rearrange

simulation results14
Simulation Results
  • Simulation of Forecasting A Weekday Load
    • Output of Semigroup Channel
simulation results15
Simulation Results
  • Simulation of Forecasting A Weekday Load
    • Extrapolation
simulation results16
Simulation Results
  • Regression Load Forecasting Results
conclusions
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