Electrical. load forecasting using artificial neural network kohonen methode. Galang Jiwo Syeto / EEPIS-ITS ITS

Transcription

1 Electrical load forecasting using artificial neural network kohonen methode Galang Jiwo Syeto / EEPIS-ITS ITS

2 INTRODUCTION Electricity can not be stored in a large scale, therefore this power must be provided when needed. As a result there is a problem in unfixed electrical power quota,, how to operate an electric power system that always able to meet the power demand at any time, in a good quality.

3 INTRODUCTION The first prerequisite should be implemented to achieve the goal,the electric company must knows the electrical load or power demand in the future. So that s s why we need, ELECTRICAL LOAD FORECASTING

4 Final project Objectives Build electrical load forecasting system more accurate in minimum average error Compare hybrid backpropagation kohonen and hybrid counterpropagation kohonen in electrical forecasting system

5 Problems How to determine algorithm using backpropagation with kohonen and counterpropagation with kohonen for electrical forecasting and get minimum error How to determine the number of hidden layer in backpropagation and counter propagation methode

6 Limitations Issue Used Artificial Neural Network especially backpropagation,counterpropagation and kohonen. Input data used is the electrical load data taken from PLN Company, Channeling and Load Management Center division for East Java and Bali between September,1 st 2005 until January,30 th 2006 Static input data in.txt file

7 System design (ANN Architecture) Hybrid methode backpropagation-kohonen kohonen 2 node in input layer,4 node in hidden layer,2 node in output layer (BP) 122 node in input layer and 122 node in output layer kohonen

8 System Design (General Method) First,we calculate mean and standard deviation each day Second,we calculate normalization profile each day Third, Get the prediction for the mean and standard deviation for next day Get the prediction

9 System design (ANN Architecture) Hybrid methode counterpropagation-kohonen 2 node in input layer,4 node in hidden layer,2 node in output layer (CP) 122 node in input layer and 122 node in output layer kohonen

10 System Design (Data) Data for this system was electrical load data taken from PLN Company, Channeling and Load Management Center division for East Java and Bali between September,1 st 2005 until January,30 th per hour in mega-watt units,total of the data are 3648 data

11 System Design (Data) PERIODE I NPUT PERIODE O UTPUT DATA TRAINING DATA TES

12 Implementation (BP initialization) Initial 3 weight randomize between 0 until 1 Initial Alpha for backpropagation Maximum error value Sigmoid function value (lambda) Initial Alpha for kohonen Epoch value for kohonen

13 Implementation (CP initialization) Initial 3 weight randomize between 0 until 1 Initial Learning Rate alpha and beta width neighbors controller Funct ction (k0), Number of counterpropagation Epoch Initial Alpha for kohonen Epoch value for kohonen

14 Implementation (data preprocessing) Normalization Mean and deviation standart

15 Implementation (data preprocessing) Normalization Profil

16 Implementation (NN Training) Backpropagation Counterpropagation Get the best weight for the mean and standar deviation forecasting

17 Implementation (Classification) Classify mean and deviation standart in 122 group classification Classify mean and deviation standart result of the forecasting Get the normalization profil index

18 Implementation (Postprocessing) Use euclidiance distance to get the nearest normalization profil index

19 Implementation (Forecasting Result) Get the forecasting result using

20 Implementation (Mean Square Error) To assess the performance of this forecasting system, we use MSE for getting error calculation MSE = (Actual i - Fitted i ) 2 /n

21 Testing and Analyzation Determine the number of neuron in hidden layer (backpropagation-kohonen) kohonen) Jumlah Neuron MSE Training 0, , , , , MSE Peramalan , , , , ,8643

22 Testing and Analyzation Determine the number of neuron in hidden layer (counterpropagation-kohonen) Jumlah Neuron MSE Training 0, , , , , MSE Peramalan , , , , ,1022

23 Testing and Analyzation Compare forecasting value hybrid from backpropagation kohonen and counterpropagation kohonen One next step forecasting metode CPNN-Kohonen BPNN-Kohonen MSE , ,4288

24 Testing and Analyzation Compare forecasting value hybrid from backpropagation kohonen and counterpropagation kohonen Five next step forecasting metode MSE CPNN-Kohonen BPNN-Kohonen , ,2535

25 Testing and Analyzation Compare forecasting value hybrid from backpropagation kohonen and counterpropagation kohonen Ten next step forecasting metode CPNN-Kohonen BPNN-Kohonen MSE , ,5660

26 Testing and Analyzation Compare forecasting value hybrid from backpropagation kohonen and counterpropagation kohonen Fifteen next step forecasting metode CPNN-Kohonen BPNN-Kohonen MSE , ,5583

27 Testing and Analyzation Compare forecasting value hybrid from backpropagation kohonen and counterpropagation kohonen Twenty next step forecasting metode CPNN-Kohonen BPNN-Kohonen MSE , ,9772

28 Testing and Analyzation Compare forecasting value hybrid from backpropagation kohonen and counterpropagation kohonen Twenty five next step forecasting metode CPNN-Kohonen BPNN-Kohonen MSE , ,8640

29 Testing and Analyzation Compare forecasting value hybrid from backpropagation kohonen and counterpropagation kohonen Thirty next step forecasting metode CPNN-Kohonen BPNN-Kohonen MSE , ,8463

30 Testing and Analyzation Compare forecasting value hybrid from backpropagation kohonen and counterpropagation kohonen mse BPNN-KOHONEN CPNN-KOHONEN tahap peramalan

31 Conclusion 1. Electrical load forecasting using hybrid backpropagation kohonen better than counterpropagation kohonen 2. Forecasting system that hybrid Counterpropagation Kohonen, at the training process,, if the number of neurons used in the hidden layer increase,give effect increasing the error in forecasting result. 3. Results of a small error when the training has not given effect produce a small error value at the end of the forecast. 4. For get the best result of forecasting depend on number of hidden layer that used when training process