Artifitual Neural Networks Ppt Seminars

INTRODUCTION

• Load forecasting is one of the central functions in power system operation

• Need for accurate Power Demand Forecast began during the Industrial revolution

• Power Demand forecasting came into prominence during the Energy Crisis in the 1970’s

OBJECTIVE

• To achieve the goals of optimal planning and operation of power system, knowledge of future system load is necessary

IMPORTANCE OF LOAD FORECASTING

• Unit commitment• Maintenance scheduling• Economic operation of power plants• Fuel purchase• Allocation of spinning reserve

TYPES OF LOAD FORECASTING

• Long term load forecasting• Short term load forecasting• Very short term load forecasting

FACTORS AFFECTING LOAD

• Meteorological conditions • Economic and Demographic factors• Time factors• Random factors

METHODS OF LOAD FORECASTING

• Time-Trend Method• Time-Series Method• Regression Method• Expert System• Expert Opinion Method• End-Use Method• Econometric Method• Artificial Neural Network

COMPARISION OF ANN AND CONVENTIONAL TECHNIQUES

IMPORTANT FEATURES

TIME SERIES METHOD

REGRESSION ANALYSIS

NEURAL NETWORKS

LOAD INFORMATION CONSIDERED CONSIDERED CONSIDERED

WEATHER INFORMATION

IGNORED CONSIDERED CONSIDERED

FUNCTIONAL RELATIONSHIP BETWEEM LOAD AND WEATHER VARIABLES

IGNORED REQUIRED NOTREQUIRED

COMPLEX MATHEMATICAL CALCULATION

REQUIRED REQUIRED NOT REQUIRED

TIME REQUIRED FOR PREDICTION

MORE MORE LESS

ADAPTABILITY LESS LESS MORE

BACK PROPAGATION

Most widely and frequently used neural network learning algorithm

BP training is mathematically designed to minimize the error

BP is a SUPERVISED TRAINING technique SUPERVISED TRAINING is predictable and easy

to use.

NETWORK TRAINING

• The process of determining the network parameters to achieve the desired objective

• A significant amount of historical data is used to train the network

• Learning process is performed epoch-by-epoch basis until the weights stabilize and

• Error converges to minimum value

ANN MODEL

• 26 INPUTSa) 24 hour load data for day d-1 d is the day of

the forecastb) Day index(1-7) representing the day of the

week of the day D-1.c) Month index(1-12) representing the month of

the day • 24 0utputs 24 hour load data for day D

ERROR MEASURE

In order to have a qualitative analysis of the models the following types of errors were used as a yardstick to measure the performance of the models.

Let Y(i) (i=1,2,……24)denote the predicted hourly load and

T(i) (i=1,2,……24) denote the actual hourly load.

• Mean Absolute Percentage Error (MAPE): It denotes the absolute percentage error averaged for the twenty-four hours.MAPE= (1/24) * ∑ [ abs { (Y(i)-T(i) ) / T(i) } ] * 100 , i=1,2,….24.

• Root Mean Square Error (RMSE): It denotes the square root of the mean of the

squares of the individual hourly errors.RMSE= (1/24) * [ ∑ { ( Y(i)-T(i) ) / T(i) }2 ]1/2 * 100 , i=1,2,….24.

ERROR COMPARISON

EFFECT OF NUMBER OF NEURONS

The MAPE average is lowest when the number of neurons is 5

EFFECT OF NUMBER OF HIDDEN LAYERS

A single hidden layer with five neurons is found to beThe optimum combination

MONTH AND YEAR

MAPE(%)

DECEMBER 2002 4.22

JANUARY 2003 4.20

FEBRUARY 2003 3.62

MARCH 2003 3.59

APRIL 2003 3.79

MAY 2003 5.41

JUNE 2003 4.29

JULY 2003 4.36

AUGUST 2003 4.16

SEPTEMBER 2003 5.17

OCTOBER 2003 4.59

NOVEMBER 2003 4.33

AVERAGE 4.31: Monthly MAPE for One Year

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