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ANN based STLF of Power System

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Short Term Load Forecasting using ANN
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International Journal of Computer Applications (0975 - 8887)
Volume 30- No.4, September 2011
Artificial Neural Network based Short Term Load
Forecasting of Power System
Salman Quaiyum, Yousuf Ibrahim Khan, Saidur Rahman, Parijat Barman
Department of Electrical and Electronic Engineering,
American International University - Bangladesh,
Banani, Dhaka - 1213.

ABSTRACT
forecasting applications. These algorithms are better than back-
propagation in convergence and search space capability.
Load forecasting is the prediction of future loads of a power
system. It is an important component for power system energy
2. ARTIFICIAL NEURAL NETWORK
management. Precise load forecasting helps to make unit
An Artificial Neural Network (ANN) is a mathematical or
commitment decisions, reduce spinning reserve capacity and
computational model based on the structure and functional
schedule device maintenance plan properly. Besides playing a
aspects of biological neural networks. It consists of an
key role in reducing the generation cost, it is also essential to the
interconnected group of artificial neurons, and it processes
reliability of power systems. By forecasting, experts can have an
information using a connectionist approach to computation. An
idea of the loads in the future and accordingly can make vital
ANN mostly is an adaptive system that changes its structure
decisions for the system. This work presents a study of short-
based on external or internal information that flows through the
term hourly load forecasting using different types of Artificial
network during the learning phase.
Neural Networks.
General Terms
2.1 Recurrent Neural Network
A Recurrent Neural Network (RNN) is a class of neural network
Artificial Intelligence, Neural Networks.
where connections between units form a directed cycle. Unlike
Keywords
feed-forward neural networks, RNNs can use their internal

memory to process arbitrary sequences of inputs. A recurrent
Load Forecasting, Power System, Particle Swarm Optimization.
neural network consists of at least one feedback loop. It may
consist of a single layer of neurons with each neuron feeding its
1. INTRODUCTION
output signal back to the inputs of all the other neurons.
Load forecasting is one of the central functions in power
In this work, Elman's recurrent neural network has been chosen
systems operations and it is extremely important for energy
suppliers, financial institutions, and other participants involved
as the model structure which has shown to perform well in
in electric energy generation, transmission, distribution, and
comparison to other recurrent architectures [8]. Elman's network
supply. Load forecasts can be divided into three categories:
contains recurrent connections from the hidden neurons to a
short-term forecasts, medium-term forecasts and long-term
layer of context units consisting of unit delays which store the
forecasts. Short-term load forecasting (STLF) is an important
outputs of the hidden neurons for one time step, and then feed
part of the power generation process. Previously it was used by
them back to the input layer.
traditional approaches like time series, but new methods based
on artificial and computational intelligence have started to
replace the old ones in the industry, taking the process to newer
heights.
Artificial Neural Networks are proving their supremacy over
other traditional forecasting techniques and the most popular
artificial neural network architecture for load forecasting is back
propagation. This network uses continuously valued functions
and supervised learning i.e. under supervised learning, the actual
numerical weights assigned to element inputs are determined by
matching historical data (such as time and weather) to desired
outputs (such as historical loads) in a pre-operational "training
session". The model can forecast load profiles from one to seven
days.
Evolutionary algorithms such as, Genetic Algorithm (GA) [1, 2],
Particle Swarm Optimization (PSO) [3-5], Artificial Immune
System (AIS) [6], and Ant Colony Optimization (ACO) [7] have
been used for training neural networks in short term load

Fig 1: Elman recurrent neural network topology.
1

International Journal of Computer Applications (0975 - 8887)
Volume 30- No.4, September 2011
Figure 1 is an Elman recurrent neural network topology where w
the leader and each particle keeps track of its coordinates in the
denotes a vector of the synaptic weights, x and u are vectors of
problem space. This fitness value is stored which is referred to
the inputs to the layers, m is the number of input variables, and r
as pbest. Another "best" value tracked by the particle swarm
is the number of neurons in the hidden layer.
optimizer, is the best value obtained so far by any particle in the
neighbors of the particle. This location is called lbest. When a
The weighted sums for the hidden and the output layers are:
particle takes all the population as its topological neighbors, the
best value is a global best and is called gbest.
(1)
(7)
(2)
where,k = [1,r], n = [1,N], and N is the number of data points
(8)
used for training of the model. The outputs of the neurons in the
hidden layer and output layer are computed by passing the
where, Vi is the current velocity, t defines the discrete time
weighted sum of inputs through the tan sigmoid and pure linear
interval over which the particle will move, is the inertia
transfer functions respectively.
weight, Vi-1 is the previous velocity, presLocation is the present
location of the particle, prevLocation is the previous location of
Mathematically, the outputs of the hidden layer and the output
the particle and rand( ) is a random number between 0 and 1. c1
layer can be defined as:
and c2 are the learning factors, stochastic factors and
acceleration constants for "gbest" and "pbest" respectively.
(3)
(4)
where,K is a coefficient of the pure linear transfer function.
Another training parameter considered is the momentum factor
as an attempt to prevent the network to get stuck in a shallow
local minimum. Equation (5) shows how the synoptic weights
are adjusted and how the network determines the value of the
increment
on the basis of the previous value of the
increment
(5)

The other important training parameter is the learning rate which
controls the amount of change imposed on connection weights
during training and to provide faster convergence.
Mathematically, the weights are updated using the equation:
(6)

where, is the learning rate.
Fig 2: PSO-ERNN Learning Process
Figure 2 shows the learning process of PSO-ERNN (Particle
3. PARTICLE SWARM OPTIMIZATION
Swarm Optimized - Elman Recurrent Neural Network). The
PSO as a tool that provides a population based search procedure
learning is initialized with a group of random particles in step 1,
in which individuals called particles change their position (state)
which are assigned with random PSO positions (weight and
with time. In a PSO system, particles move around in a
bias). The PSO-ERNN is trained using the initial particles
multidimensional search space. During flight, each particle
position in step 2. Then, it produces the learning error (particle
carries out position adjustment in accordance to its own
fitness) based on initial weight and bias in step 3. The learning
experience, to the experience of a neighboring particle. This
error at current epoch is reduced by changing the particles
helps make use of the best position of the particle encountered
position, which updates the weight and bias of the network. The
by itself and its neighbor. Thus, a PSO system combines local
"pbest" and "gbest" values are applied to the velocity update
search methods with global search methods, attempting to
according to (7) to produce a value for positions adjustment to
balance exploration and exploitation.
the best solution or targeted learning error in step 4. Step 5 has
the new sets of positions (weight and bias) produced by adding
The basic concept is that for every time instant, the velocity of
the calculated velocity value to the current position value. Then,
each particle, also known as the potential solution, changes
these new sets of positions are used to produce new learning
between its pbest(personal best)and lbest(local best) locations.
error in PSO-ERNN. This process is repeated until the stopping
The particle associated with the best solution (fitness value) is
2

International Journal of Computer Applications (0975 - 8887)
Volume 30- No.4, September 2011
conditions of either minimum learning error or maximum
4.

DATA
COLLECTION
AND
number of iterations are met which is shown in step 6.
PREPROCESSING
3.1 Global best PSO
A simple data collection method was employed to ensure
Global version of PSO is faster but might converge to local
adequate historical samples of the load. Load Data used in this
optimum for some problems. The Local version, though slower
work were collected from the Bangladesh Power Development
does not easily get trapped into a local optimum. We
Board (BPDB) and ISO New England.
implemented the global version to achieve a quicker result. The
position of a particle is influenced by its best visited position
4.1 Data Scaling Methods
and the position of the best particle in its neighborhood.
Data scaling is carried out in order to improve interpretability of
network weights. The equation below has been adopted and
Particle position, xi, was adjusted using
implemented to normalize the historical load data.
(9)
(12)
where the velocity component, vi, represents the step size.
The load value is normalized into the range between 0 and 1 and
For the basic PSO,
then the neural networks are trained using the suitable algorithm.
Neural networks provide improved performance with the
normalized data. The use of original data as input to the neural
network may increase the possibility of a convergence problem.
(10)
4.2 Data Storage
where, w is the inertia weight, c1and c2 are the acceleration
Data can be stored in many ways. In this work, the collected
coefficients, r1,j, r2,j ~ U(0,1), yi is the personal best position of
data was stored in Microsoft Excel worksheets. The worksheets
particle i, and is the neighborhood best position of particle i.
were then imported into MATLAB using the command
"xlsread".
If a fully-connected topology is used, then i refers to the best
position found by the entire swarm. That is,
4.3 Composition of the Input Vector of the
prediction models

(11)
The structure of the input vector specifies the selected
endogenous and exogenous variables. In the work of T. Gowri
where,s is the swarm size.
Monohar et al. [9] five inputs were selected from the previous
day and five each from the previous weeks on the same day
The pseudo-code for PSO is shown below:
were selected to predict the load of the next day. If data points
for each particle i 1,...,s do
were insufficient, the forecasting would be poor. If data points
were useless or redundant, modeling would be difficult or even
Randomly initialize xi
skewed [10].
Set vi to zero
The input vector (IV) structure used here, consists of two
Set yi = xi
previous hours active power values, L(t-1) and L(t-2) and some
homologous consumption past load values of the previous week
endfor
L(t-168) and L(t-169). The inclusion of these values provides
Repeat
information regarding the consumption trend in the past
for each particle i 1,...,s do
homologous periods [11]. It was found that load for 24 hours
and 168 hours are highly correlated. The structure of IV is given
Evaluate the fitness of particle i, f(xi)
in Figure 3.
Update yi
Update using equation (11)
for each dimension j 1,...,Nddo
Apply velocity update using (10)
endloop
Apply position update using (9)
endloop
Until some convergence criteria is satisfied


Fig 3: Composition of the input vector (IV) (non-weather
and weather sensitive model)
3

International Journal of Computer Applications (0975 - 8887)
Volume 30- No.4, September 2011
The ISO New England historical load data was first collected
from their website. The load data was taken every hour for a
period of one week. The training data was defined from
Monday, 31st March till 6th April, 2008 and the corresponding
target was defined for the period from 7th to 13th April, 2008.
The models were also evaluated for generalization capability,
thus testing data set was defined from the first week of March
3rd to 9th. After correlation analysis, Dry Bulb and Dew Point
temperatures were used as the input for the Elman Weather
Sensitive Model.
5. SIMULATION AND RESULTS
For evaluating our proposed load forecast model, several neural
network architectures were implemented in MATLAB version

7.10.0.499 (R2010a). Feed Forward Network and Elman
Recurrent Network - these two networks were implanted
Fig 4(a): The performance goal met
according to their default architectures as provided in
MATLAB. In addition, Jordan Recurrent Network was created
Load Forecasting: Day Model
using the custom network creation process. Before starting the
4900

training of the networks, each layer was individually initialized
Actual load values
using the initnw function.
Predicted load values
4800
Then each model was trained using traingdx (Gradient descent
with momentum and adaptive rule backpropagation) training
4700
algorithm with the help of Neural Network Training tool. After
]
W
that each network was simulated and their performance was
M
[
4600
d
observed. Finally, Elman Network was trained using Particle
a
o
L
Swarm Optimization method. Mean Square Error (MSE)
4500
performance measure function was employed along with other
functions like MPE (Mean Percentage Error) and MAE (Mean
Absolute Error).
4400
(13)
4300 1
2
3
4
5
6
7
Day

(14)
Fig 4(b): Forecasting Result of the maximum demand
(15)
-3
x 10
Network Error
0
where, Lactual (n) is the actual load, Lpredicted (n) is the forecasted
value of the load, and N is the number of data points.
-0.5
5.1 Daily Maximum Demand Prediction
For maximum demand prediction, the data that was collected
-1
from BPDB was insufficient for training of the network using
r
o
r
r
traingdx. Therefore, the network was trained using trainlm
E
(Levenberg-Marquardt
backpropagaton).
This
network
-1.5
performed better - it achieved smaller MSE than the network
trained with traingdx.
-2
-2.51
2
3
4
5
6
7
Day

Fig 4(c): Actual Network Error


4

International Journal of Computer Applications (0975 - 8887)
Volume 30- No.4, September 2011
5.2 Forecasting with ISO New England Load
-3
x 10
Network Error
4
Data
3
4
x 10
168 hours ahead STLF using training data
1.8

Actual load values
2
1.7
Predicted load values
1.6
1
r
1.5
o
r
]
r
0
W
E
k
[
1.4
d
a
o
L
-1
1.3
-2
1.2
1.1
-3
1 0
20
40
60
80
100
120
140
160
180
-4
Time [hrs]

0
20
40
60
80
100
120
140
160
180
Time [hrs]

Fig 4(d): Output of Feedforward Network
Fig 4(g): Network Error (Elman)
-3
x 10
Network Error
4
5.3 Particle Swarm Optimized Elman
3
Recurrent Neural Network (PSO-ERNN)
-4
2
x 10

average error
1
error goal
r
o
r
r
0
3
E
-1
e
c
n
a
m
-2
2
r
o
f
r
e
P
-3
1
-40
20
40
60
80
100
120
140
160
180
Time [hrs]

0 0
0.5
1
1.5
2
2.5
3
Fig 4(e): Network Error (Feedforward)
Epochs


Fig 4(h): The performance goal met
4
x 10
168 hours ahead STLF using training data
4
168 hours ahead STLF using training data
1.8

x 10
1.8

Actual load values
Actual load values
1.7
Predicted load values
1.7
Predicted load values
1.6
1.6
1.5
1.5
]
]
W
W
k
[
k
[


1.4
1.4
d
d
a
a
o
o
L
L
1.3
1.3
1.2
1.2
1.1
1.1
1
1
0
20
40
60
80
100
120
140
160
180
0
20
40
60
80
100
120
140
160
180
Time [hrs]
Time [hrs]


Fig 4(i): Output of PSO-ERNN
Fig 4(f): Output of Elman Network
5

International Journal of Computer Applications (0975 - 8887)
Volume 30- No.4, September 2011
For future works, the error in the network can be further reduced
Network Error
0.02
if a larger dataset is used for network training. Also, the load
forecasting model can be improved by including other weather
0.015
parameters like temperature, wind speed, rainfall etc.
0.01
7. ACKNOWLEDGMENT
0.005
We would like to thank Engr. K. M. Hassan, CSO, BPDB for
providing us with necessary information.
0
r
o
r
r
E -0.005
8. REFERENCES
[1] Heng, E.T.H., Srinivasan, D., Liew, A.C., "Short term load
-0.01
forecasting using genetic algorithm and neural networks",
Energy
Management
and
Power
Delivery,
1998.
-0.015
Proceedings of EMPD '98. 1998 International Conference
-0.02
on, Volume 2, 3-5 March 1998, Page(s):576 - 581 vol.2.
-0.025
[2] Worawit, T., Wanchai, C., "Substation short term load
0
20
40
60
80
100
120
140
160
180
forecasting using neural network with genetic algorithm",
Time [hrs]

TENCON '02. Proceedings. 2002 IEEE Region 10
Conference on Computers, Communications, Control and
Fig 4(j): Network Error (PSO-ERNN)
Power Engineering; Volume 3, 28-31 Oct. 2002,
Page(s):1787 - 1790 vol.3.
Table 1: Comparison between different network
[3] Azzam-ul-Asar, ul Hassnain, S.R., Khan, A., "Short term
performances
load forecasting using particle swarm optimization based
Network
Training
MSE
MAE
MPE
ANN approach", Neural Networks, 2007. IJCNN 2007.
Epoch
International Joint Conference on ; 12-17 Aug. 2007,
Feedforward
8
2.0351e-6
0.0012
0.0011
Page(s):1476 - 1481.
Jordan
3
4.6124e-6
0.0018
0.0021
[4] Wei Sun, Ying Zou, Machine Learning and Cybernetics,
Elman
7
1.868e-6
0.0012
9.1269e-4
"Short term load forecasting based on BP neural network
Elman (WS)
8
2.3895e-6
0.0013
1.8288e-8
trained by PSO", 2007 International Conference on;
PSO-ERNN
3
1.6296e-5
2.6042e-3
7.1934e-3
Volume 5, 19-22 Aug. 2007, Page(s):2863 - 2868.

[5] Bashir, Z.A., El-Hawary, "Short-term load forecasting
using artificial neural network based on particle swarm
From the above table, it can be seen that PSO-ERNN is faster
optimization
algorithm",
Electrical and Computer
but slightly more prone to errors (with MSE); but this model
Engineering, 2007. CCECE 2007. Canadian Conference
could outperform others if more data sets were used. This
on; 22-26 April 2007, Page(s):272 - 275.
network could handle large amount of data in a short amount of
time. The Elman Weather sensitive model performs better but it
[6] You Yong, Wang Sun'an, Sheng Wanxing, "Short-term
takes longer time than Simple Elman network. Dry bulb and
load forecasting using artificial immune network", Power
dew point temperatures had a very weak correlation with the
System Technology, 2002. Proceedings. PowerCon 2002.
load, so their presence did not have any significant effect on
International Conference on; Volume 4, 13-17 Oct. 2002,
forecasting. Providing different weather parameters would have
Page(s):2322 - 2325 vol.4.
definitely increased the network performance. Overall this table
shows that there is always a trade-off between the networks and
[7] Chengqun Yin, Lifeng Kang, Wei Sun, "Hybrid neural
network model for short term load forecasting", Third
depends on the amount of data, quality of data, time required
and most importantly design requirements and designers.
International Conference on Natural Computation, 2007.
[8] Almedia L.B. et al., "Parameter adaptation in stochastic
6. CONCLUSION
optimization", On-line learning in neural Networks (Ed. D.
Several neural network models for short-term load forecasting
Saad), Cambridge University Press, 1998.
were studied in this work. According to the discussion and the
comparison of model forecast accuracy shows that Particle
[9] T.Gowri Monohar and V.C. Veera Reddy, "Load
Swarm Optimized Elman Recurrent Neural Network (PSO-
forecasting by a novel technique using ANN", ARPN
ERNN) is the best model for 168 hours ahead load forecasting.
Journal of Engineering and Applied Sciences, VOL. 3, NO.
This type of network can be very efficient in terms of predicting
2, April 2008.
future loads.
[10] Lendasse, J. Lee, V. Wertz, M. Verleysen, "Time Series
Though the simulations seemed very promising, the models
Forecasting using CCA and Kohonen Maps - Application
developed here still need to be tested on data sets from other
to Electricity Consumption", ESANN'2000 proceedings -
sources, so that reliability of these models can be verified for
European Symposium on Artificial Neural Networks
other load patterns.
Bruges (Belgium), 26-28 April 2000, D-Facto public.,
ISBN 2-930307-00-5, pp. 329-334.
6

International Journal of Computer Applications (0975 - 8887)
Volume 30- No.4, September 2011
[11] P.J. Santos, A.G. Martins, A.J. Pires, J.F.Martins, and R.V.
Illam Region", International Journal of Electrical,
Mendes, "Short Term load forecast using trend information
Computer, and Systems Engineering, Volume 1, 2007,
and process reconstruction", International Journal of
pp.1307-5179.
Energy Research, 2006; 30:811-822.
[15] T.Gowri Monohar and V.C. Veera Reddy, "Load
[12] Simaneka Amakali, "Development of models for short-term
forecasting by a novel technique using ANN", ARPN
load forecasting using artificial neural networks", Cape
Journal of Engineering and Applied Sciences, VOL. 3, NO.
Peninsula University of Technology Year, 2008.
2, April 2008.
[13] Ayca Kumluca Topalli, Ismet Erkme, and Ihsan Topalli,
[16] George I Evers, "An automatic regrouping mechanism to
"Intelligent short-term load forecasting in Turkey",
deal with stagnation in particle swarm optimization",
International Journal of Electrical Power & Energy
Graduate thesis for the degree of Master of Science,
Systems, Volume 28, Issue 7, September 2006, pp. 437-
University of Texas-Pan American, pp. 35-40, 2009.
447.
[17] S. Sumathi, Surekha P., "Computational Intelligence
[14] Mohsen Hayati and Yazdan Shirvany, "Artificial Neural
Paradigm: Theory and application using MATLAB", CRC
Network Approach for ShortTerm Load Forecasting for
Press, 2010.






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