stlf presentation
TRANSCRIPT
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Presented by :-Sukhpreet Kaur
07MCP013
M.Tech (2nd Year)
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Problem definition :Problem definition :--Short Term Load Forecasting :-
Load forecasting is very important for
power system planning and security. The main problem for planning is the
determination of load demand in
future.
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Contd.Contd.There are three types of load forecasting:-
Long term :- for years in future.(for planning like capacity expansion, price
and regulatory policy decisions) Medium term :- one month to few years
(energy marketing, maintenance scheduling
etc.) Short term:- for 1 hour, 1 day, 1 week.
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Short term load forecastingShort term load forecasting
Short term load forecasting is basically
prediction of load demand of a power
system hour by hour for one day or oneweek in the future.
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Need of STLF :Need of STLF :--1. Short term unit maintenance
scheduling2. Economic scheduling of generating
capacity
3. Scheduling of fuel purchase
4. Security analysis
5. Unit commitment6. Demand side management
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Techniques of STLFTechniques of STLF1. Traditional statistical load forecasting
techniques
i. Regression
ii. Time series
iii. Kalman filters etc.
2. Modern Techniquesi. Expert system
ii. Artificial Neural networks
iii. Fuzzy logiciv. Fuzzy neural networks.
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STLF using Fuzzy Neural NetworksSTLF using Fuzzy Neural Networks
Artificial neural networks are widely
used in short term load forecasting. Theycan handle non-linearity between electric
load and weather factors but they lack to
handle unusual changes that occur in theenvironment.
Fuzzy logic systems were proved to be
successful in handling imprecise data butthey lack the ability to learn from
experience.
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Artificial Neural Network :Artificial Neural Network :--yA artificial neural network is massively
parallel-distributed processor made up ofsimple processing units called neurons,
which have a natural tendency for storing
experimental knowledge. The motivationfor the development of neural network
technology stems from the desire to
develop an artificial system that couldperform intelligent tasks similar to those
performed by the human brain.
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Contd.Neural networks resemble the human
brain in the following two ways:1. A neural network acquires
knowledge through learning.
2. A neural networks knowledge is
stored within interneuron connection
strength known as synaptic weights.
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ANN neuron:-
where
Oj is the output of a neuron;
fj is a transfer function, which is differentiable and nondecreasing, usually represented using a sigmoid
function
such as a logistic sigmoid, a tangent sigmoid, etc.
wjk is an adjustable weight that represents the
connection
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Fuzzy logic:Fuzzy logic:--Fuzzy logic is a superset of
conventional (Boolean) logic thathas been extended to handle theconcept of partial truth - truth values
between "completely true" and"completely false". It was introducedby Dr. Lotfi Zadeh of U.C. Berkeleyin the 1960's. Fuzzy logic is the waythe human brain works, and we canmimic this in machines so they willperform somewhat like humans
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Fuzzy Neural NetworksFuzzy Neural Networksy Unification of ANN & FL is called fuzzy
neural network.y The fuzzy neural network arises from
the need to overcome the lengthy
learning process and poor convergence of traditional neural
networks (typically BP neural
networks) and urgent needs to extract
fine knowledge from a large amount of
original data.
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Types:Types:--y Depending upon the level of
integration between ANN and FLmany separate FNN models can be
constructed. Broadly they are
divided into two categories:1. Where the weights are fuzzy
2.Where input data is fuzzified butweights are not fuzzy.
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Basic configuration of a FNN
Fuzzy rule base
Defuzzificationinterface
Fuzzificationinterface
Fuzzy inference machine
Nonfuzzy
input
Nonfuzzy
output
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Basic blocks of FNNy FUZZIFICATION INTERFACE:- it provides a link
between the non fuzzy outside world and fuzzy
system framework. it converts input signals fromexternal state to internal fuzzy state i.e. it converts
real valued data into fuzzified representation.
y FUZZY RULE BASE:- It is a set of linguistic rules
or conditional statements in the form IF asset ofconditions are satisfied, THEN a set of
consequences are inferred.
y
FUZZY IN
FEREN
CE MACHIN
E:- it is a decisionmaking logic performing the inference operations of
fuzzy rules.
y DEFUZZIFICATION INTERFACE:- defuzzifies
fuzzy output and generates a non fuzzy crisp
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Fuzzy sets and fuzzy decision
y For simplicity, the max-membership decision rule is
applied for decision making
.
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Fuzzy neuron:-y We suppose the following standard form for fuzzy
neurons
:-
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In FNN, we have five types of
neurons
1) Input neuron:- An input neuron is placed
in the input layer in FNN
. It has only oneinput. No knowledge is stored in an input
neuron. It has no threshold T. We
suppose it as
y = x (2)
So that output equals input for an input
neuron.
2) Knowledge neuron:- A knowledge neuron
is placed in the front part of the
knowledge layer. Its role is just to store
knowledge.
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Contd.2) Category neuron A category neuron is placed in
the knowledge layer. Its role is to produce the
degree of membership for one knowledgecategory. This membership value for the
knowledge category is equal to that of the point
with the largest membership function value
(possibility) in one knowledge category.
n is the number of inputs to a category neuron.
It shows that there are n knowledge elements
under this knowledge category.
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Contd1. Output neuron An output neuron is placed in the
output layer. Its role is to produce a definite
output 1 or 0 according to its own threshold t,where t is sent from a threshold neuron in the
output layer.
Each output neuron in the output layercorresponds to one output category. If the output
representing a category is 1, then it says the
current input vector belongs to this category,
otherwise not .
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Contd.y Threshold neuron There is only one threshold
neuron in the output layer. A threshold is to produce
a dynamic and changeable threshold for eachoutput neuron. Its function is the same as that of a
category neuron in the knowledge layer. It has the
form
Where n is the input number of a threshold
neuron.
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FNN structure:-
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Contd
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ContdThere are three layers:-
1. Input layer:-The input layer receivesthe input vector and transmits It to
the knowledge layer
2. Knowledge layer:-The knowledgelayer stores a knowledge and
processes it
3. Output layer:-The output layer treats
(defuzzifies) the output values from
the knowledge layer
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Contd
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Learning Algorithms:-y Step 0 - Input the operation mode to decide to train
or test.
y Step 1 Read in the input number and outputnumber.
Read in the number of total samples.
Define the input vector and output vector.
Define the sample array.
Define a buffer to store all the samples.
Let the learning count = 0.
y Step 2 Let the learning count increase by 1.y Step 3 Training Stage Circulation begins. The
circulation
ends when all samples have been studied.Then,
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Forecasting Procedure:-y The basic forecasting procedures to apply FNN for
STLF are as follows:
(1) Define the input and output variables as statevariables, control variables and output variables attime t+1, t, t-l,..., t-p. where p is the number ofperiods of time lag. In the proposed fuzzy neural
network, there are 1000 outputs which represent1000 possible output categories in the unit range[0,1] from 0.001, 0.02,..., to 0.998, 0.999, 1.000.
(2) Set up a series of input/output samples from
historical data.
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Forecasting Procedure (Contd)
(3) By training the model with formed samples, we
can obtain an approximate discrete simulationmodel from limited (not unlimited) but rich historicaldata.
(4) Use the established simulation model to conductload forecasting hour by hour.
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Forecasting Procedure (Contd)* This is just principal procedure.
Forecasting performance dependsupon many other considerations like:-
how to select proper input variables,
how to smooth original data overdifferent years,
how to handle the outputs from FNN
to produce the best forecasting value
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How to select proper input
variables:-Four types of factors affect load demand at time t+1.
They are past load, past weather, day type, and
time. The four kinds of factors can be furtherdivided more exactly as follows.
1. load: load at time t, t-1, t-2,...,t-p.
2. weather: weather type, temperature, humidity,
wind speed, wind direction, sky cover (rain ornot), snow or not) at time t, t-1, t-2,..,t-p.
3. day-type: weekdays-weekends, holidays at timet, t-1, t-2,..,t-p
4. time: season, month, week, date, hour,
where p is the number for time-lag effect.
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Different input variables are:-y In the forecasting computation for two large
neighboring electric utilities, the inputs include
1. Month code2. Week code
3. Hour code
4. Holiday code with 3 hour time lag5. Temperatures at five sites (three large cities
and two metropolitan airports) with time
lag of 5.
6. Relative humidity at the two airports
7. Load with 27-hour time lag
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How to handle outputs from
FNN:-y FNN is employed as the core algorithm for the
STLF model. To have a satisfactory forecasting
performance, we establish 1000 categories in theoutput layer. The 1000 outputs represent possibleforecasting values from 0.001, 0.002,... to 0.998,0.999. Each output is accompanied by a
membership value indicating the possibility of thisoutput.
y Basic rule used is:-
Step 1. select the best three outputs with the
largest three whole memberships.Step 2. select a final output with the largest loadmembership among the best three outputs as theforecast value
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Numerical results:-
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Numerical results:-
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Conclusion:-A fuzzy neural network has been applied to
solve the problem of STLF . With FNN's
capacity in simulating nonlinearity and its
high flexibility in model maintenance, a new
simulation forecasting model of short-term
load forecasting has been created. Aftermore investigating in data smoothing,
variable selecting and output handling, the
simulation forecasting model shows manyadvantages.
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Thanks