International Journal of Research in Management, Science & Technology (E-ISSN: 2321-3264)
Vol. 1; No. 2, December 2013
Available at www.ijrmst.org
Mid Term Electrical Load Forecasting For
State of Himachal Pradesh Using Different
Weather Conditions via ANN Model
Anand Mohan
Ph.D Research Scholar, University of Petroleum & Energy Studies, Dehradun, India.
[email protected]
Abstract- Mi d-term forecasting of l oad demand is
necessary for the correct operation of electric utilities.
There is an on-g oing attenti on toward putting new
approaches to the task. Recentl y, Neurofuzzy modeling
has played a successful role in vari ous applications over
nonlinear ti me series prediction. This paper presents a
neurofuzzy model for l ong-term load forecasting. This
model is identified through Locally Linear Model
Tree(LoLi MoT) learning alg orithm. The model is
compared to a multilayer perceptron and hierarchical
hybri d neural model (HHNM). The models are trained
and assessed on load data extracted from state load
dispatch center, Tutu, Shimla, India.
Keywords- MTLF, ANN, Load Forecasting, MAPE and
Max APE.
and long term load forecasting. The periods for these
categories are not defined clearly in literature. Thus
different authors use different time periods to define these
categories. But roughly, short-term load forecasting covers
hourly to weekly forecast. These forecasts are often needed
for day to day econo mic operation of power generating
units.
Medium-term load forecasting deals with predictions
ranging fro m weeks to a year. Maintenance of plants and
networks are often roofed in these types of forecast.
Long term forecasting on the other hand deals with forecast
fro m few months to one year. It is primarily intended for
capacity expansion plans, capital investments and corporate
budgeting. These types of forecasts are often co mplex in
nature due to future uncertainties such as planning and
extension of existing power system networks for both the
utility and consumers required long- term forecasts.
I. INTRODUCTION
Power system planning starts with the forecast of load
requirements. With the fast growth of power systems
networks and increase in their co mplexity, many factors
have become in fluential in electric power generation,
demand or load management. Load forecasting is one of the
major factors for economic operation of power systems.
Future load forecasting is also important for network
planning, infrastructure development and so on. However,
power system load forecasting is a two dimensional
concept: consumer based forecasting and utility based
forecasting. Thus the significance of each fo recasting can
be handled disjointedly. Consumer based forecasts are used
to provide some guidelines to optimize network p lanning
and to reduce operational costs. In basic operations for
power generation plant, forecasts are needed to assist
planners in making strategic decisions with regard to unit
commit ment, hydro-thermal coordination and interchange
evaluation and security assessments and so on. This type of
forecast deals with the total power system loads at a given
time and is normally performed by utility companies.
Power system load forecasting can be class ified into three
categories, namely, short term, medium term
2321-3264/Copyright©2013, IJRMST, December 2013
II. LOAD FORECASTING USING ANN
A large variety of artificial intelligence and statistical
techniques have been developed for load forecasting.
Some of the methods like; Similar day approach,
Regression methods, Time series, Neural network,
Expert systems, Fuzzy logic are used now a days.
Artificial Neural Network
The use of art ificial neural network has been widely
studied electric load forecasting technique since 1990.
Neural networks are essentially non-linear circuits its
outputs is some linear or nonlinear mathemat ical
functions of its inputs. The inputs may be the outputs
of the other network elements as well as actual
network inputs. In pract ice network elements are
arranged in a relatively small numbers of connected
layers of elements between network inputs and outputs.
Feedback paths are sometimes used.
In applying a neural network to electric load forecasting,
one must select the architecture (e.g. Hopfield, mu lti
layer, Bolt zmann mach ine) the number and connectivity
and elements, use of bi-
60
International Journal of Research in Management, Science & Technology (E-ISSN: 2321-3264)
Vol. 1; No. 2, December 2013
Available at www.ijrmst.org
directional links and the format (e.g. b inary or continuous)
to be used by inputs and outputs, and internally.
The most popular ANN arch itecture for electric load
forecasting is mu lti layer emp loying back p ropagation
algorith m. Back propagation use continuously valued
functions and supervised learning.
That is, under
supervised learning, the actual nu merical weights assigned
to element inputs are determined by matching historical
data (such as time and weather) to the desired outputs (such
as historical electric loads) in a preoperational “train ing
session”. Artificial neural networks with unsupervised
learning do not require teacher signal in the training
session.
Start
The reliability and short comings of the
forecasting methodology.
Analyzing different weather conditions
affecting forecasting.
III. PROBLEM FORMULATION & METHODOLOGY
Following steps have been followed by the investigator to
formulate the above said problem:
Collection of data-base of the previous
months for load forecast.
i) First of all historical weather and load data is scrutinized.
All monthly and daily pred ictions have been read and any
bad data is removed.
Training of Data Using ANN model of
MATLAB.
ii) Then monthly deviations have been calculated excluding
some special days e.g. holidays, Saturdays and Sundays.
iii) Then data base has been created by the investigator for
developing load forecasting model.
Calculation of errors for each month for
each model.
iv) Accordingly temperature and hu midity have been
differentiated as maximu m, minimum and average.
v) Also the rainy season and predicted rainfall has been
considered for making the algorith m for long term load
forecasting.
Calculation of MAPE and MaxMAPE.
vi) A fter this classification some ANN technique have been
used to train these input variables for getting the e xpected
outcome. System has been simu lated with the help of
MATLAB/ SIMULINK.
Comparison of results for two proposed
models.
vii) Then mean absolute percentage error (MAPE) has been
calculated for the given forecasting model.
Stop
The flow chart for methodology adopted is given in Fig. 1.
Fig. 1 Proposed model for ANN-based load forecasting.
For both the models inputs are same i.e. six and data for
both the models is same. The collected data has been shown
in Table 1.
2321-3264/Copyright©2013, IJRMST, December 2013
61
International Journal of Research in Management, Science & Technology (E-ISSN: 2321-3264)
Vol. 1; No. 2, December 2013
Available at www.ijrmst.org
TABLE I
mu ltilayer feed forward neural netwo rk t rained by
implementing backpropagation technique. Backpropagation
is created by generalizing the Widrow-Hoff learning rule to
mu ltip le-layer networks and non-linear d ifferentiable
transfer function. Input vectors and target vectors are used
to train a network until it can appro ximate a function.
Networks with biases, a sigmoid layer, and a linear layer
are capable o f appro ximat ing any function with a fin ite
number of discontinuities.
Backp ropagation refers to the manner in which the g radient
is co mputed for non-linear mult ilayer networks. There exist
a number of variations on the basic algorith m that are based
on other standard optimizat ion techniques. MATLAB has
the property of imp lementing a number of these variations.
As said previously that backpropagation gives very good
answers when presented with inputs never seen before by
them. Th is property of generalization makes it possible to
train a network on giving set of input/target pairs and get
good output. So this model is being imp lemented using the
Neural Network Toolbox of Matlab.
Firstly the data to be given to neural network is div ided into
two groups as input and other as target. Two excel sheets
have been prepared for input and target which is then stored
as variable in MATLA B. Further 75% data has been taken
as training data and 15% data has been taken as testing
data. Then comes the normalization of data. Normalizat ion
is the process of making data such that it is acceptable to
the system or software which we are using. Normalizat ion
is very important in case of neural networks because they
take data in the range of -1 to +1.
Now one of the keys for designing good ANN model is
choosing appropriate input variables. In the proposed model
there are six inputs and one output. There are six neurons in
the input layer and only one neuron for output layer. There
doesn’t exist any specific formu la for calculation of hidden
layer neurons. It has been observed by the researcher that
too many h idden layers can cause the network to memo rize
the scheme rather than fo recasting the actual output and too
few h idden layer neurons can cause the network to not learn
the convergence. By going through traing and testing of
data many times it has been found that for the given data
the number of hidden layers should be one and number of
neurons in the hidden layer has been taken as 20.
VALUES OF VARIOUS AFFECT ING WEATHER FACTORS FROM
APRIL, 2011 – AUGUST, 2011
Mon
th
April
,
2010
May,
2010
Jun,
2010
July,
2010
Aug,
2010
Sept.
,
2010
Oct.,
2010
Nov.
,
2010
Dec.,
2010
Jan.,
2011
Feb.,
2011
Mar.,
2011
April
,
2011
May,
2011
June,
2011
July,
2011
Aug.
,
2011
Max
.
Te m
p.
Min
Te m
p.
Max.
Humid
ity
Min.
Humid
ity
Rainf
all
Sno
w
26.5
15.2
50.4
44.4
5.2
0
27.9
16.4
45.3
34.6
39.7
0
26.7
15.5
63.5
52.9
218
0
24.2
16.6
89
80.2
490.6
0
23.2
16.6
92.3
86.2
388.2
0
22.7
14.8
84.8
79.4
391.8
0
22.7
11.6
68.8
57.8
15
0
19.7
8.7
64.7
49.4
17.3
5.2
15.6
4.5
60.4
41.7
49.6
24.8
12.8
2.5
56.3
42.8
14.7
3.6
14.3
4.5
66.4
51.1
55.9
2.5
19.7
8.7
52.2
38.9
37.6
0
21.7
2
7.16
52.93
41.8
19
0
26.8
14.9
52.1
41.38
80
0
25.5
13.7
55
40.2
32
0
24.4
15.6
60.5
43.5
21.4
0
6333.
33
6530.
16
6654.
33
6543.
66
20.5
15.9
58.7
54.4
19.6
0
6743.
22
O utp
ut
5889.
4
6100.
38
5962.
43
6423.
86
6466.
30
6266.
57
6257.
06
6001.
53
6850.
43
6921.
95
6243.
12
6232.
17
IV. ARTIFICIAL NEURAL NETWORK (ANN)
METHOD USING MATLAB
Neural networks exh ibit characteristics such as mapping
capabilit ies or pattern association, generalization,
robustness, fault tolerance and parallel and high speed
processing. Neural networks learn by examp les. They can
also be trained with known examp les of a problem to
acquire knowledge about it. Once trained successfully, the
network can be put to effective use in solving unknown or
untrained instances of the problem.
Though neural networks have been broadly classified as
single layer and mult ilayer feed fo rward networks, here a
load forecasting technique has been developed by using a
2321-3264/Copyright©2013, IJRMST, December 2013
V. ERROR AND MAPE CALCULATION
By using the model developed by the researcher, load, error
and % error of all the months have been calculated.
Then error and % error is found out using the below given
formula.
Error = Output by NN – Actual Output
% Error = ( Error / Actual Output ) x 100
62
International Journal of Research in Management, Science & Technology (E-ISSN: 2321-3264)
Vol. 1; No. 2, December 2013
Available at www.ijrmst.org
Actual Output, Output by NN, Error and % Error has been
given in Table 2.
VI. CONCLUSIONS
In this proposed work, the researcher has proposed model
for MTLF for the state of Himachal Pradesh. This one is
based on Artificial Neural Netwo rks. Real t ime data fro m
state of Himachal Pradesh has been used to illustrate the
proposed model. Output demand, error and % error have
been calculated for both the models.
MATLAB / SIM ULINK software is used to realize the
given data for both the models. For the first model Neu ral
Network Tool o f MATLA B software is used. By giving six
inputs taking into consideration the different weather
conditions for the state output demand, error and % error
have been found out. Then MAPE and Max A PE have been
found out. For this model MAPE and Max APE are 2.582
% and 19.09 % respectively. Graphs have been plotted
between output by NN and actual output and for % error for
showing the results graphically.
Also its imp lementation in Himachal Pradesh is a urgent
requirement because in this state the load forecasting is
done manually by State Load Dispatch Centre, Tutu,
Shimla and weather p lays an important role in load
forecasting in this state as the researcher has observed that
snow, humidity and rainfall is affecting the output results at
particular instances as shown by the proposed model
TABLE II
Month
April,2010
O utput by
NN
5898.551688
Actual
O utput
5889.4
Error
9.151687806
% Error
0.155392532
May, 2010
6112.439409
6100.38
12.0594095
0.197682923
7100.789055
5962.43
1138.359055
19.09219991
July, 2010
6420.893917
6423.86
Aug, 2010
6464.179226
6466.3
2.966082854
2.120774276
0.046172906
0.032797338
Sept.,2010
6270.761223
6266.57
4.191222943
0.066882249
Oct., 2010
6492.624039
6257.06
235.5640387
3.764771933
Nov.,2010
6002.413083
6001.53
0.883083007
0.014714298
Dec., 2010
6851.280678
6850.43
Jan., 2011
6921.778749
6921.95
Feb., 2011
6498.39285
6243.12
0.850677739
0.171250516
255.2728502
0.012417874
0.002474021
4.088866628
Mar.,2011
7003.816775
6232.17
771.6467
12.38803
0.022500403
Jun, 2010
April,2011
6331.904975
6333.33
1.425024792
May, 2011
6538.367826
6530.16
8.207826095
0.125691041
216.6089631
38.30976381
3.390941853
3.255158117
0.585448569
0.050286686
June, 2011
6437.721037
6654.33
July, 2011
6505.350236
6543.66
Aug.,2011
6739.829058
6743.22
REFERENCES
[1]
[2]
A graph has been plotted with actual output and output by
NN model as shown in Fig below & % error calculated has
been plotted in Fig. 2.
[3]
Actual Output Vs Output by NN
8000
[4]
7500
[5]
[6]
% Error
7000
[7]
6500
[8]
[9]
Output by NN
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Actual Output
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Apr.,10
May,10
Jun.,10
Jul.,10
Aug.,10
Sep.,10
Oct.,10
Nov.,10
Dec.,10
Months
Jan.,11
Feb.,11
Mar.,11
Apr.,11
May,11
Jun.,11
Jul.,11
Aug.,11
Sep.,11
Fig. 2 Plot representing actual output vs output by ANN
2321-3264/Copyright©2013, IJRMST, December 2013
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