This document provides a review of forecasting methodologies used in restructured electricity markets. It discusses various time series forecasting models including AR, MA, ARMA, ARIMA, and neural network models. It also describes hybrid models that combine different techniques, such as weighted nearest neighbor (WNN) and fuzzy neural network (FNN) models. The goal of the document is to analyze different forecasting methods that can be used in deregulated power systems with competition in wholesale and retail electricity markets.

1. 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering
(UPCON)
978-1-5386-5002-8/18/$31.00 ©2018 IEEE
Forecasting Methodology Used in Restructured
Electricity Market: A Review
Aishvarya Narain
Department of Electrical Engineering
Madan Mohan Malaviya University of Technology
Gorakhpur, U.P. India
aishvarya.n89@gmail.com
S.K.Srivastava
Department of Electrical Engineering
Madan Mohan Malaviya University of Technology
Gorakhpur, U.P., India
sudhirksri05@gmail.com
Abstract— Electricity Forecasting are very important for
electricity generation and transmission market for buyer and
supplier and distribution market for consumers point of view.
Globally there is a huge change in trends towards deregulation
and restructuring in power industry over last fifteen years.
Competition in wholesale market and retail market together give
open access to the transmission network can give many
advantages to the consumers. Accurate model and planning are
require for forecasting the electricity in deregulated power
market. These advantages include lower electricity prices and
better services. This paper present a review on deregulation in
restructured electricity market and different forecasting
methodology used in deregulated power system.
Keywords—Electric power deregulation, forecasting
technology,Hybrid model, soft computing model, power exchange
I. INTRODUCTION
Electricity is exchangeable quantity which can be traded
like rice, pulse etc but there is a remarkable difference
between physical aspects of electricity and other traded
quantities Many people are frustrate when they do not have
choices to buy when it comes to the field of electricity in many
states, it can be solve by simply regulated to the electricity
company to our area [1-3]. There are lot of alternatives
present, but they are grouped under headings like "Transcos,"
"Gridcos," "ISO/PX," "ISOs," and finally, organizations for
soothing transmission.Some state has step in and started
deregulation of electricity, which means that companies which
are interested to survive in those market they have to come
and offer us their services in the competitive market.
Electricity deregulation is very important with respect to,
consumers when it comes to the selection of buyers as they
stuck with the company they have getting electricity form.
Even if they are getting poor service still they have to pay
costs to their poor services, they have no option but to
continue as it is because there is no any choice to select.
When there will be competition in the electricity market,
customers have the option to choose another company rather
than the one they are with. The result of this is that lower in
the prices from their provider and also better customer service.
When there will be no competition, and then there will be fear
of losing their customers so they treat their customer however
they want without losing those [4-5]. Deregulation solves this
kind of problem as there will be more choices to the
customers. The company uses the many primary sources,
some of them use wind and solar power for their primary
source and some uses the coal and nuclear as primary source
for power generation.
There are many companies who generate the electricity and
so after deregulation we have choice to buy it from many
different electric suppliers. No matter which supplier we
choose for electric generation local utility will continued to
deliver electricity, maintain poles and wires in the area read
electric meters and if there is any emergency we will have the
facilities to call local utility [6-8]. So now power lies in our
hand we can choose our supplies smartly to get good quality
electricity with minimum cost.
II. FORECASTING METHODOLOGY
A. Time Series Modelling
The spectrum based procedure is used for model analysis.
This procedure consists of mainly two steps i.e.
• The parameter of the model is estimated.
• This estimation helps in predicting the power
spectrum.
In time series model there will be numerical data points
which are in sequence and successive order. The interval is
uniform in this case [9-12]. Generally a sequence of numbers
is collected at regular interval basis and it is plotted against the
period of time. This is use for analyzing the time series data
for meaningful characteristic of data collected.
B. Types of Component
There are changes which belong to time series
components. These changes are affected by the economics,
socials, naturals, industrials and political reasons [13]. These
reasons which effect the time series is called component of
time series. There are mainly four type of component are use
namely
1) Series with trends (secular trends): The component
observation is increase or decrease regularly through time.
2) Series with seasonality (seasonal variation): The
component observation stays high and get low, this pattern
follow periodically.
3) Series with cyclic component (cyclical variation): There is
business cycle. For example we can take recessions.
4) Random variation (irregular variation): Random variation
emphasis the unpredictable component that shows the
irregularity in time series graph [14]. They are appearing in
zigzag formats.

2. III. TIME SERIES MODEL FOR FORECASTING
A. AR Model (Autoregressive Model)
The auto regression means the regression of variable itself.
It is the random process as the output variable depends upon
the previous values [6, 12, 15] It is analyze in the frequency
domain and generally has only poles i.e. no zeros are present
in the system.
The equation of AR model can be given by “(1)”
( ) = + ∑ (∅ ) +∈ (1)
Where ∅ … … … … . . ∅ represents the parameter of the
model or coefficient of recursive filter. C is constant. ∈ Is
known as white noise or output correlated errors. “ ” is order
of the model which uses to determine order of the
autoregressive model and is determined by minimizing the
mean square error. For this we have to solve linear equation. If
the error is decrease the order of model is increases. In the
other term we can say that the choice of the number of terms
in the AR model is acknowledged as order “p” of the model. If
the order is low then we get smooth spectrum and if it will be
high we gets the peak in the spectrum.
Akaike (1969) propose the criteria for selection of model
of order “p”, known as Finite Prediction Error (FPE) criteria
which are use for longer data “(2)”.
=
( )
( )
(2)
Where ‘N’ is number of samples and “p” is order of the
model. In 1974, Akaike proposed the second criteria named as
Akaike Information Criteria (AIC) which are uses for short
data given by “(3)” to “(5)”.
= + (3)
If the value of is decrease the also decrease which
results in the increase in the order of model. If “p” increases
the value of also increases. Autoregressive model of order
“p” can be written as
= + ∅ + ∅ … . . … . . ∅ +∈ (4)
This equation is known as ( ) Model. It is a very
flexible model. By changing the parameter ∅ … … … … . . ∅
we get the different time series parameter. ∈ Change only the
scale of the series not the parameter of the series.
For an ( ), there will be no dependency in the term and
only errors and noise are present in the system called as white
noise.
For an ( ), there is two case i.e. as ∅ > 0, ∅ > 0 is
positive, the previous term and noise contribute for output.
When ∅ , ∅ close to ‘0’, the process still look line white
noise but when ∅ , ∅ close to ‘1’ the previous term
contributes more than the noise for getting smooth output.
This output is similar to low pass filter output.
B. Moving Average
Moving average is one of the important methods of
measurement [6, 13, 15]. If we want to measure the trends a
unit we need to calculate moving average. The meaning of
moving average is known as the average of any subset of
number for measuring the long term trends. The moving
average of order “p” is given by
= ∑ (5)
Where p=2k+1, i.e. when we take a “k” period of time “t”,
the average value gives the estimated value of trend cycle at
time t. This value is very close to ideal value and average;
estimate the randomness of the data which results smooth
trends cycle. If we say p-MA it means a moving average of
order p. Fig shows the graph of electricity sale for South
Australia from the year 1998 to 2017 on x axis and electricity
consume in GWh on the y axis.
Fig. 1. Electricity sale for South Australia from year 1998 to 2017[13]
The moving average of the 5-MA is shown in the fig.1.As
it is taken by successive average of five year. There is no
average of first two year and last two year because we are not
having the observation for it. That’s why graph did not start
from starting and not close at the end. It can be easily seen
from the graph that moving average is smoother than the
original graph. The bold line graph shows the moving average
of order 5 shown on the fig.2.
Fig. 2. Moving average of 5-MA electricity sale for South Australia from year
1998 to 2017[13]
If we get the increasing order of moving average the
smoothness of the graph also increases. So for higher order we
get approx straight line for the same system analysis.
C. ARMA Model (Autoregressive Model)
Autoregressive moving average model i.e. ARMA (m, n)
is used for univariate time series modeling and it is
combination of autoregressive, AR (m) and moving average,
MA (n) model[6, 12, 14, 15]. In the Autoregressive model i.e.
AR (m) model m past observation and constant values with
random error is use for future value estimation.
Mathematically it is given by “(6)” to “(12)”.
= + ∑ + (6)
= + + + ⋯ + + (7)
Here " " is actual value,” " is random error or shock at
“t” time period,” ....... " are model parameters, “c” is
constant and “m” is order of the model. AM (m) model use
past value of time series, but in case of MA (n) model the past
error of time series is used to evaluate the variable. The
mathematical equation is given by

3. = + ∑ ⍺ ∈ +
= + ⍺ ∈ + ⍺ ∈ + ⋯ + ⍺ ∈ + (8)
Where “ ” is mean of the series, “⍺ … … … . ⍺ ” are the
model parameter, “n” is the order of the model, “ " is white
noise or random shocks whose mean is “0” and variance is
“1’ or constant value.
Moving average model fitting is more complicate than the
autoregressive model for time series analysis because random
error analysis is quite difficult. AR (m) and MA (n) model is
combine to form ARMA (m, n) model for time series analysis.
Mathematically it can be given by
= + + ∑ + ∑ ⍺ ∈ (9)
Here AR model order is “m” and MA model order is “n”.
Lag operator is used for manipulating the ARMA model. It is
defined as “L = ”
Polynomial lag operator for ARMA model is given by
AR (m) model = ( ) , MA (n) model = ⍺( )
For ARMA (m, n) ( ) = ⍺( ) (10)
Here
( ) = 1 − ∑ (11)
And
⍺( )=1+ ∑ ⍺ (12)
Autoregressive model has an important property of
inevitability i.e. AR (m) can be represented in terms of MA
(∞) but if the roots of the equation ⍺ ( ) = 0 lie outside the
unit circle then this phenomenon is known as invertible
condition of MA (n) mode.
D. ARIMA Model:
Autoregressive Integrated Moving Average Model [7, 9].
ARMA model is used only for stationary data in time series
model but in practical life the nature of data are non-
stationary. To overcome these shortcomings ARIMA model is
used which is nothing but integrated version of ARMA model
used for non-stationary date time series model.
In ARIMA model, level of differencing “d” is use for
converting non-stationary data into stationary data. The
mathematical formula for ARIMA (m, d, n) model is given by
“(13)” and “(14)”.
( )(1 − ) = ⍺( ) (13)
i.e.
(1 − ∑ ) (1 − ) = (1+ ∑ ⍺ ) (14)
Here m, d, n are order of autoregressive, integrated and
moving average model which are either equal to zero or greater
than one and “d” is level of differencing. When d=0, then it
reduce to ARMA model i.e. ARMA (m, n).
ARIMA(m,0,0) means AR(m) model and ARIMA(0,0,n)
means MA(n), ARIMA(0, 1, 0) is known as random walk
model use for non stationary data.
The next version of ARIMA model is known as
autoregressive fractionally integrated moving average model
(ARFIMA model). As ARIMA model is use only the non
seasonal non stationary data.
For use of seasonal date one model is proposed known as
SARIMA (seasonal autoregressive integrated moving average)
model.
All these models are stochastic method for forecasting and
time series analysis. These methods are effective but require
lots of information. Computational cost is also very high.
IV. ARTIFICIAL NEURAL NETWORK (ANN) FOR
FORECASTING
It is another alternative technique for time series
forecasting. The main objective of ANN model is to construct
a model replica of intelligence of human brain into machine.
ANN recognize the pattern of data and regularity in the input
data just like human brain[5, 13].ANN get the solution from
the previous experience and provides the result based on the
previous knowledge [3].
ANN is non-linear inherently which make them practical
and more accurate for analysis the complex data [2]. This
method is much more helpful than linear approach such as
ARIMA model. With the help of ANN any continuous
function can be approximated into desire accuracy. ANN use
parallel processing data information to approximate a function
of larger class to get high accuracy. If the input data are fuzzy
or the input data are incomplete then ANN can deal with the
situation to perform the forecasting.
Multilayer preceptors (MLPs) are used for forecasting i.e. a
single hidden layer feed forward network [5, 8]. The three
layer i.e. input, hidden (middle) and output layers are
connected by acyclic links to characterize the model. The node
present in the layer name as processing element. One or more
than one hidden layers are possible. The ANN with different
layers is shown below in fig.3.
Fig. 3. ANN with input, hidden or middle and output layers [4]
The mathematical formula of output of ANN model is given
by “(15)”.
= +∑ ( + ∑ )+∈ ,∀ (15)
Where is the output, is the inputs as “i” varies from
“1 to p”, “q” is hidden nodes. And are connected
weights as “j” varying from “0 to q” and “i” varying from “0
to p”. ∈ Is random shock, and are bias terms. g(x) is
nonlinear activation function such as hyperbolic tangent,
Gaussian function etc.
V. HYBRID MODEL
Artificial neural network is basic model, there are lots of
combinational technique is use to make hybrid model for
forecasting [16-18]. These technique are named below
A. Weighted nearest neighbor technology (WNN)
Weighted nearest neighbor technology (WNN) algorithm
is based on the similarities shows by individuals in a

4. population for pattern classification [19, 20]. The individual
which have similar properties are put together in a circle
which surrounds the population. WNN classifier rule analyse
and learn to get the idea. Thus an unclassified sample point is
assignment by the nearest neighbour’s decision rule. It is
different from other statically method which uses information
of data base to get the appropriate model where as in the WNN
the training sets are consider as a model [21].
Now a day’s WNN method are used in medical diagnosis
tools, time series forecasting, game theory and expert system.
B. Fuzzy neural network technology (FNN)
A fuzzy neural network system is a linear machine that is
use for finding the parameter of fuzzy system i.e. fuzzy rule
and fuzzy set with the help of approximation technique used in
neural network [22]. Fig.4. shows Structural diagram of FNN.
Fig.4. Structural diagram of FNN [23]
The similarity in the fuzzy system and neural network are
that both use the pattern recognition and regression technique
for solving the problem if mathematical model are not given
[23, 24].
C. Adaptive wavelet neural network technology (AWNN)
WNN first used by Zhang et al [25] in the place of
classical feed forward neural network (FFNN). Wavelet neural
network uses the property of FFNN and wavelet theory for
approximation of arbitrary non-linear function. Wavelet has
the property of adapting the wavelet shape training data where
as other classical FFNN has the property to adopt fixed shape
training data. Hence WNN is more suitable for modeling of
high frequency signal. It is used in the field of non-linear
system identification [26], function learning [27], load
forecasting for short term [28], time series prediction [29].
Wavelet transform can be divided into two categories i.e.
continuous wavelet transform (CWT) and discrete wavelet
transform (DWT).
The CWT ( , ) of function ( ) w.r.t. a mother wavelet
∅( )is given by equation “(16)” and “(17)”.
( , ) =
∅
( )∅ ,
∗ ( )
∞
∞
(16)
Where,
∅ ,
∗ ( ) =
√
∅ , , , ∈ , > 0 (17)
The scale parameter a controls the spreading and b
determine central position of wavelet. Here ∅ ,
∗ ( )is basic
function obtained from varying the mother wavelet.
D. Hybrid of Neural Network and Fuzzy Logic technology
(NNF)
It is a homogeneous function and resemble neural network.
The fuzzy system is illustrated as special kind of neural
network. The main advantage of this system is its architecture
as both the network needs not to communicate each other.
They are ones complementary. The system are used to learn
online and offline. Fig.5. shows hybrid fuzzy neural network
Fig.5. Hybrid Fuzzy Neural Networks[28]
The fuzzy set is use as weights input variable, output
variable and rules are models as neurons which are included or
excluded depend upon the situation. Finally network neurons
represent the fuzzy rule base.
For fuzzy controller, membership function is required.
Triangular are Gaussian shape can be consider as membership
function. The membership function has a arbitrary set of
parameter. To generalising the data, optimization of these
functions is required for fuzzy system. To solve this problem
neural network is used. By fixing the shape of membership
function, the neural networks optimize their parameter by
gradient descent [30].
E. Cascade Neuro-evolutionary Algorithm technology
(CNEA)
There are two type of forecasting is used in day-ahead
prediction. They are names as iterative forecasting and direct
forecasting.
In iterative forecasting a single forecaster is used for
forecast with one output node and the value obtain from it is
again use as input for the next forecast value. Where as in the
direct forecasting the number of output node is equal to
forecast horizon length and further value is obtain from
forecast output [31]. Both the methods have some merit and
demerit. To overcome the demerits CNEA comes into the
picture.
Fig. 6. CNEA Structure [33]
Fig.6. Shows that CNEA consist 24 forecasters in which
each of the forecaster has a single output to predict the price of
one hour for next day. The structure of CNEA is the
combination of direct and iterative forecasting where price of
each hour is forecast directly and output is also one [32, 33]. It
is shown that there are 24 forecasters and each one has the
combination of NN and EA, where NN has multilayer
preceptron (MLP) structure for prediction and EA is used for
search and find better solution.

5. F. Combination of Wavelet transform, particle swarm
optimization and adaptive network based Fuzzy
interference system technology (WPA)
It is the hybrid approach for forecasting [34, 35]. It is the
combination of wavelet transform (WT), particle swarm
optimization (PSO), and adaptive network based fuzzy
interference system (ANFIS). This approach is use when
system shows non-linear behavior and time variant complex
function.
G. Some Other hybrid system are
• Combination of Neural Network with Wavelet
Transform (NNWT)
• Hybrid intelligent method based on the Wavelet
Transform
• Hybrid intelligent system technology (HIS)
It is found that forecasting using hybrid model gives better
results compare to the other traditional methods. Hybrid model
can be use in both the condition i.e. linear and nonlinear
condition.
TABLE I. COMPARISTION OF HYBRID MODEL FOR FORECASTING
METHEDOLOGY RULE USED DATA TYPE
EXPERIMANTAL DATA
TAKEN FROM(To Calculate
MCP,MCQ,LMP,SMP,)
REFERENCES
RESULTS OBTAIN BY
CALCULATING
WNN
Nearest Neighbour’s
Decision Rule
Unclassified
Sample
Spanish electricity market [19] MRE,MAE,MMRE
Victorian power system [20] MAPE
Ontario, New England and Italian
electricity markets [21] MAPE
FNN Approximation Technique
Fixed Shape
Training Data
UK power pool [22] MAPE
California Energy Markets [23] SDE,MAE,MAPE
Spanish Electricity Market [24] WME, DME
AWNN Wavelet Theory
Wavelet Shape
Training Data
Spain market, PJM (Pennsylvania,
New Jersey, and Maryland) market
[25] DMAPE,WMAPE
Wavelet Neural Network, Mlp
And Rbf Networks
[26]
1D & 2D Function
Learning
Historical Load and Weather
Information
[27] APE
Taiwan power [28] LSFE
NNF Fuzzy Rule Base
Triangular are
Gaussian shape
Numerical Examples [30] IE
CNEA
Combination Of Wavelet
Theory, Approximation
Technique and Nearest
Neighbour’s Decision Rule
Random Sample
/Mixed Data
North American and Slovakian
electric utilities
[31] MAPE, MSE, ME
EEX market [32] RMSE,MAE,MAPE
Spain market, PJM market [33] MAPE
PJM market [34] WME,WPE
WPA
Combination Of Wavelet
Theory, Approximation
Technique and Fuzzy Rule
Base
Input data based on
correlation analysis
Mainland Spain Market [35] MAPE
SMP: System Marginal Price
LMP: Locational Marginal Price
MCP: Market Clearing Price
MAPE: Mean Absolute Percentage Error
SDE: Standard Deviation of Error
MAE: Mean Absolute Error
WME: Weekly Mean Error
DME: Daily Mean Error
DMAPE: Daily Mean Absolute Percentage Error
WMAPE: Weekly Mean Absolute Percentage Error
MRE: Mean Relative Error
MAE: Mean Absolute Error
MMRE: Mean Error Relative To “P” Month
RBF: Radial Basis Function Network
MLP: Multi-Layer Perceptron
APE: Average Percentage Error
LSFE: Least-Squared Fitting Error
IE: Inference Errors
MSE: Mean Square Error
ME: Mean Error
EEX: European Energy Exchange
WME: Weekly Mean Error
WPE: Weekly Peak Error
MCQ: Market Clearing Quantity

6. Table I. Shows the important basic deference, rule used,
input data type, data taken for case study of different
electricity market and compared parameters. As the time
changing, new technologies are taking place for better result.
Although different researcher take different data for validating
their technique but Catalão [35], compare almost all technique
for same data and compare it taking real example. It is found
that WPA is better than other hybrid models.
VI. CONCLUSION
Deregulation in electricity market is very important.
Forecasting is the very important term for deregulation
aspects. In this paper comparison of various time series
models, ANN model and various hybrid models has been
presented. From the analysis it has been found that hybrid
model can give better results from time series and ANN
models as it contain properties of both the models. Among the
hybrid model from one example it is found that WPA gives
better results. A recent and largely used technique support
vector machine/ machine learning based hybrid models can be
used. The forecasting can be very helpful to predict power and
ATC calculation for relieve congested lines in deregulated
system.
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