With increased competitiveness in power generation industries, more resources are directed in optimizing plant operation, including fault detection and diagnosis.

1. ARTIFICIAL INTELLIGENCE
IN POWER STATIONS
PRESENTED BY
SOMARLAPATI CHAITANYA AVINASH
somarlapatichaitanya@gmail.com

2. DRAFT
 AI as a Revolution.
 Load Forecasting definition and process
involved in it.
 Types of load forecasting.
 Techniques used to calculate load
forecasting.
 Conclusion.
 Bibliography.

3. ARTIFICIAL INTELLIGENCE
Artificial Intelligence is the science of exhibiting intelligence
by machine currently achieved by humans.

4.  John McCarthy is one of the "founding fathers" of artificial intelligence,
together with Marvin Minsky, Allen Newell, and Herbert A. Simon.

5. TIMELINE OF AI
AI is coined as a proposal
for the first time by
John McCarthy
1955
DARPA and NASA’s
exploration and Research
begun
2004
MNC’s takeover very
important and crucial
projects.
2019
The future prediction says
that AI is going to occupy
70% jobs.
2030
First National Conference
of the American
Association for Artificial
Intelligence (AAAI) held at
stanford.
1980

6. 2000 ~ 2018
Observation 1 : The Artificial Intelligence gained
it’s initial boost.
2022 ~ 2025
Observation 2 : The Machine is dominating the
work of human. 2025
2022
2018
2000 82%
70%
64%
48%
WORLD STATISTICS
AI as a Revolution
Machine
Human

7. NEED FOR
AI ARTIFICIAL
INTELLIGENCE
o With increased competitiveness in
power generation industries, more
resources are directed in optimizing
plant operation, including fault
detection and diagnosis.
o One of the most powerful tools in
faults detection and diagnosis is
artificial intelligence (AI).

8. HYDRO POWER PLANT
THERMAL POWER PLANT
GAS POWER PLANT
NUCLEAR POWER PLANT
POWER
STATIONS

9. LOAD FORECASTING
Why load
forecasting is
calculated
frequently by the
Power Stations ?
The Load Forecasting helps in
planning the future in terms of the
size, location and type of the
future generating plants to meet all
the needs of future generations.
It helps in deciding and planning for maintenance of the power systems.

10. PROCESS IN L.F
Collect Information
Choose the
Forecasting Model
Verify Model
Performance
Identify the problem
Perform a
Preliminary Analysis
Data analysis
SIX STEPS
PROCEDURE

11. LOAD FORECASTING
MEDIUM
TERM
FORECASTING
SHORT TERM
LOAD
FORECASTING
LONG TERM
FORECASTING

12. SHORT TERM
LOAD
FORECASTING
ᴥ Short term load forecasting (STLF) refers
to forecasts of electricity demand (or load),
on an hourly basis, from one to several
days ahead. In the daily operations of a
power utility, the short term load
forecasting is of vital importance.
ᴥ It is required for unit commitment, energy
transfer scheduling and load dispatch.
ᴥ The short term load forecasting has played
a greater role in utility operations with the
emergence of load management
strategies.

13. ∞ Medium-term Load forecasting (MTLF)
becomes an essential tool for today
power systems, mainly in those
countries whose power systems
operate in a deregulated environment.
∞ This kind of load forecast has many
applications like maintenance
scheduling, mid-term hydro thermal
coordination, adequacy assessment,
management of limited energy units,
negotiation of forward contracts, and
development of cost efficient fuel
purchasing strategies.
MEDIUM TERM LOAD
FORECASTING

14. LONG TERM
LOAD
FORECASTING
ᴥ Long-term load forecasting is an important
component for power system energy
management and reliable power system
operation.
ᴥ Long - term load forecasting span is within
the period of one year to more than one
year.

15. Types of Forecasting Techniques
Artificial Neural Networks
Fuzzy Logic Approach
Fuzzy logic or Fuzzy systems are
logical systems for standardization
and formalisation of approximate
reasoning.
NEURO - FUZZY APPROACH
Neuro - fuzzy refers to
combinations of artificial neural
networks and fuzzy logic.
Regression Method
Regression method is a conventional approach.
01
02
03
04
Biologically inspired systems
which convert a set of inputs into
a set of outputs by a network of neurons.

16. ARTIFICIAL NEURAL
NETWORKS
¥The application of neural networks in power utilities
has been growing in acceptance over the years.
¥The main reason behind this is because the capability
of the artificial neural networks in capturing process
information in a black box manner.
¥When load forecasting is dealt by using neural
networks, we must select one of the number of the
available architectures (such as Hopfield, back
propagation, Boltzmann, etc).
¥The number of layers and elements, the connectivity
between them , usage of unilateral or bilateral links
and the number format to be used by inputs and
outputs.

17. FUZZY LOGIC APPROACH
Step 1: Compile a tentative list of input and output variables using statistical analysis,
engineering judgments and/or load forecasting.
There are three input variables which are used to forecast electric loads an output are
1. Temperature.
2. Humidity.
3. Wind speed.
Step 2: Normalization of the input and output variables is done by analyzing the input and the
output behavior the input space is mapped to the membership value.
Step 3: Select the shape of the fuzzy membership for each variable; namely the triangular,
trapezoidal, Gaussian and bell shape membership function.
Step 4: For each input and output variable, tentatively define the number of fuzzy membership
functions.
Step 5: Fuzzy logic rule base is each pair of input and output data, and it’s called training
data.
Example: If ‘temperature’ is hot and ‘humidity’ is humid and ‘wind speed’ is above average
then ‘load’ is above average.

18. FUZZY LOGIC APPROACH
Defuzzification
Fuzzification
Inference
Knowledge Base

19. NEURO – FUZZY
APPROACH
 A price - sensitive (PS) load forecaster is developed.
 This forecaster consists of two stages, an artificial neural
network based PIS load forecaster followed by a fuzzy
logic (FL) system that transforms the PIS load forecasts
of the first stage into PS forecasts.
 The first stage forecaster is a widely used forecaster in
the industry known as ANN. For the FL system of the
second stage, a genetic algorithm based approach is
developed to automatically optimize the number of rules
and the number and parameters of the fuzzy membership
functions.
 Another FL system is developed to simulate PS load data
from the PIS historical data of a utility.
 This new forecaster termed NFLF is tested on three PS
databases, and it is shown that it produces superior
results to the PIS ANNLF.

20. AGRICULTURE LOAD
FORECASTING

21. INDUSTRY LOAD
FORECASTING

22. FUTURE LOAD
FORECASTING OF INDIA
֍India planned a lot in load forecasting
related to electricity domain by 2030.
֍Firstly by 2020, India wants to succeed by
replacing the LED’s for lighting purpose in
all industrial usage.

23. LOAD FORECASTING
1. Seasonal
Use of certain utility increases or decreases.
2. Trend
On certain events.
3. Random
No specific cause.
DEPENDS ON
TIME - SERIES

24. The load forecasting has both commercial and
technical implications and if not done properly, it may
lead to bad planning and inefficient operation of the
electrical power systems.
What happens if LOAD FORECASTING
is not planned properly ?

25. CONCLUSION
Precise load forecasting is very essential for electric
utilities in a spirited environment created by the
electric industry.
In this presentation we appraise some statistical view
of artificial intelligence growth and techniques that are
used for electric load forecasting.
Neural network alone cannot work for forecasting
better. If neural network is combined with fuzzy logic
then it can handle the forecasting problems well.
The evolution in load forecasting will be achieved in
two ways. One is getting excellence in statistics and
artificial intelligence and the other is to have good
understanding of the load dynamics.

26. AI revolves
around the
globe.
֍https://ieeexplore.ieee.org/document/76685
֍Tomonobu Senjyu, Hitoshi Takara, Katsumi
Uezato, and Toshihisa Funabashi,
“OneHour-Ahead load forecasting using
neural network” , IEEE Transactions on
power systems, Vol. 17, No. 1, February
2002
֍Jagadish H. Pujar, “Fuzzy ideology based
long term load forecasting”, World Academy
of Science, Engineering and Technology 64
2010.
BIBLIOGRAPHY

27. TOP CLASS AI PROJECTS
BLUE BRAIN
PROJECT
AI
INFERENCE CHIP
IBM
NEW AI LAB
Taken by
US
Funding by
Swiss Govt
$1.3
billion
Taken by
INTEL
Chip runs
Electronic Device
Taken by
IBM
Collaboration with
MIT
$240
million
$13
Million

28. THANK YOU
For Listening my lecture
Patiently

29. Any Queries
?
WAY