The document discusses using a fuzzy neural network (FNN) for short term load forecasting of power systems. The FNN combines aspects of fuzzy logic and neural networks. It initially creates rules from historical load data and then tunes the rule parameters through training to match the load data. Test results showed the FNN can forecast future loads with accuracy comparable to neural networks but with much faster training.

1. SHORT TERM LOAD FORECASTING USING FUZZY
NEURAL NETWORKS
By
T. Jagdish Singh

2. Abstract
• The fuzzy system has the network structure and the training procedure of a
neural network and is called Fuzzy Neural Network (FNN).
• A FNN initially creates a rule base from existing historical load data.
• The parameters of the rule base are then tuned through a training process,
so that the output of the FNN adequately matches the available historical
load data.
• Test results show that the FNN can forecast future loads with an accuracy
comparable to that of neural networks, while its training is much faster
than that of neural networks

3. INTRODUCTION
• Short term load forecasting (STLF) is important for the economic and
secure operation of power systems.
• Examples include unit commitment, hydro-thermal co-ordination,
interchange evaluation and security assessment.
• An excellent discussion of the traditional STLF methods can be found in
power systems.
• Recently, artificial neural networks (ANN) have been successfully applied
to STLF.
• Due to rapid change in weather conditions and the short time required for
their development, ANN based STLF models a very attractive alternative
for on line implementation in energy control centers.

4. DESCRIPTION OF FUZZY NEURAL NETWORKS
• The basic configuration of the
fuzzy system used in this paper is
shown:
• The fuzzifcation interface is a
mapping from the observed non-
fuzzy input space U c Rn to the
fuzzy sets defined in U.
• The fuzzy rule base is a set of
linguistic rules or conditional
statements in the form of: "IF a
set of conditions is satisfied,
THEN a set of consequences are
inferred".
• The fuzzy inference machine is a
decision making logic performing
the inference operations of the
fuzzy rules.

5. • The defuzzification interface
defuzzifies the fuzzy outputs of
the fuzzy inference machine and
generates a nonfuzzy(crisp)
output which is the actual output
of the fuzzy system. The crisp
output of the fuzzy system
defined above is given by:
where

6. • The fuzzy system whose crisp output is defined by above equation can be
represented by a three layer network as shown:

7. Initialization of the parameters of the FNN
• The FNN initially includes m fuzzy rules the parameters of which are
appropriately chosen on the basis of the first m input / desired output
sample pairs.
• The parameter choosing can be considered as a rule base initialization
process.
• The initial parameters of the FNN are not randomly chosen as in neural
networks.

8. GRADIENT TRAINING OF THE FUZZY NEURAL
NETWORK
A gradient algorithm is used to estimate the FNN parameters, so that the
FNN mean square error is minimized.
with
The minimization of J(z) through a gradient algorithm
leads to the following iterations for the estimation of z:
where v is the iteration index and

9. Rule Base Adaptation
• After the rule base initialization procedure is carried out the Gradient
training algorithm is used to further train the FNN based on additional
input/desired output pairs.
• The generation of new rules establishes the rule base adaptation
mechanism which is described by the following steps:
1. Feed forward the new pattern through the FNN and compute the
corresponding firing factor Sµ(XP
)
2. If Sµ(XP
) ≥ β then leave the rule base unchanged and perform Gradient
training.
3. If Sµ(XP
) < β then create a new fuzzy rule Rm+1
perform Gradient
learning on the expanded fuzzy rule base.
• Summarizing, the overall FNN training procedure comprises three major
parts: the rule base initialization, the rule base adaptation and the
Gradient learning algorithm.

10. Advantages
• It utilizes an amount of a-priori knowledge about the known system to be
approximated
• It includes just the necessary fuzzy rules within the premise space leading
to a minimum of FNN parameters for training, and
• It progressively generates new rules, expanding the existing fuzzy rule
base according to the actual working premise regions of the particular
system.

11. ADVANTAGES OF USING FNN OVER ANN
I. First, it should be noticed that an effective approximation tool should
be capable of making use of all kind of available information (expert
knowledge)
• In most practical processes the system model is not available or it is
too complex to obtain, then the main source of information is provided
by:
a) Input/output system measurements representing the quantitative kind
of information, and
b) Human expert knowledge provided in the form of linguistic
descriptions about the system, which represent the qualitative kind of
information.
On the contrary, conventional ANN can use only
quantitative knowledge in terms of input/output data.

12. II. Secondly, the FNN parameters have clear physical meaning.
• The FNN parameters can be properly initialized to capture rule base
initialization procedure.
On the contrary, the ANN parameters have no clear
relationship to the input/output data and therefore they are randomly
selected.
• Finally concluding, it should be pointed out that the acquisition of
expert knowledge and the parameter initialization capability constitute
the two major advantages of FNN Over ANN.

13. FUZZY NEURAL NETWORK FOR SHORT TERM
LOAD FORECASTING
A. Selection of the FNN input and output variables
A six-input single-output FNN has been used for STLF. The inputs (Xi),
and the output (y), of the FNN are as follows:
• X1 = L (d, h-1)
• X2= L (d, h)
• X3 = L (d, h+l)
• X4 = L (d, 22)
• X5 = L (d, 23)
• X6= L (d, 24)
• Y = Ĺ (d + 1, h)
where, d = 1, ..., 365 is the day index,
h = 1, ..., 24 is the hour index
L (d, h) is the system average load demand during day-d, hour-
h Inputs X1, X2, X3…of the FNN are hourly loads

14. B. Training of the FNN
• The FNN used for the forecasting of the load of the h-th hour of a
particular day type is trained using a data set of input-desired output
patterns created from the available hourly load data.
• The rule base of the FNN is initialized so that it contains only one rule
(m=1) defined by first input-output pair.
• Additional rules are created during the training process
• At the end of the FNN training the rule base contains 3 rules on the
average.

15. TEST RESULTS
• The developed FNN for STLF has been tested using historical load data of
the Greek interconnected power system. The Greek interconnected power
system has a peak load of about 5.5 GW

16. EXAMPLE
Table I: Average Absolute Error on daily basis using a
Fuzzy Neural Network and an Artificial Neural Network

19. CONCLUSIONS
The following conclusions can be drawn from the comparison of the
developed model with Neural Networks:
 The FNN achieved similar performance to a neural network in short term
load forecasting.
 The training of the FNN was much faster than that of neural networks.
 The FNN can effectively incorporate linguistic IF-THEN expert rules,
whereas neural networks cannot.
Research work is under way in order to:
 incorporate weather data,
 incorporate linguistic expert rules,
 use a single multi-output FNN for 24-hour load profile forecasting

20. REFERENCES
• G. Gross and F.D. Galiana: "Short term load forecasting", Prm. IEEE, Vol.
75, No 12, pp. 1558-1573, 1987.
• C. Asbury: "Weather Load Model for Electric Demand Energy
Forecasting", IEEE Tmnsactions on Power Apparatus and Systems, Vol.
PAS-94, No 4, pp. 1111-1116,1975.
• W.R. Christiaanse: "Short-term load forecasting using general exponential
smoothing", IEEE Transactjons on Power Apparatus and Systems , PAS-
90, pp. 900-911 1971.
• C.N. Lu, H.T. Wu and S. Vemuri: "Neural Network Based Short Term
Load Forecasting", IEEE Transactions on Power Systems, Vol. 8, No 1,
pp. 336-342, 1993.

21. THANKING YOU

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