stlf presentation

8/14/2019 Stlf Presentation

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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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