Ppt On Lf Ann
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- Pages: 23
Past Work •
Design and Simulation of Three Link Robot….(at Indian Space Research Satellite Centre, Bangalore, India)
•
Load flow study of a Nuclear Power Plant….(at Rajasthan Atomic Power Station, Kota, Rajasthan, India).
•
To Design and study Linear Induction Motor (LIM) for the Magnetic Levitated Vehicle….B.Tech Thesis work… (University Gold Medal for securing First Class First Position in the University). Desired O/P
•
Temperature Control System using ANN.
•
Short Term Load Forecasting Using Artificial Neural Network…M.Tech Thesis work.
•
Short Term Load Forecasting Using Fuzzy Neural Network….(follow up of the earlier work).
G-1
Desired O/P
G
SHORT TERM LOAD FORECASTING USING ANN
What’s Load Forecasting? • Tell the Future! • Short-term load forecasting (STLF) is for hour to hour forecasting and important to daily maintaining of power plant • A STLF forecaster calculates the estimated load for each hours of the day, the daily peak load, or the daily or weekly energy generation.
Taxonomy of Load Forecasting • Spatial forecasting : forecasting future load distribution in a special region, such as a state, a region, or the whole country. • Temporal forecasting is dealing with forecasting load for a specific supplier or collection of consumers in future hours, days, months, or even years.
Taxonomy of Load Forecasting (Cont) • Temporal forecasting: Long-term load forecasting (LTLF): mainly for system planning. Typically covers a period of 10 to 20 years. Medium-term load forecasting (MTLF): mainly for the scheduling of fuel supplies and maintenance. Usually covers a few weeks. Short-term load forecasting (STLF): for the daytoday operation and scheduling of the power system.
WHY Short-term load forecasting • An central problem in the operation and planning of electrical power generation. • To minimize the operating cost, electric supplier will use forecasted load to control the number of running generator unit. • STLF is important to supplier because they can use the forecasted load to control the number of generators in operation shut up some unit when forecasted load is low start up of new unit when forecasted load is high.
(HOW) Forecasting Methods • Expert Judgments • Linear Models • Linear Regression • Time Series Approach
• Nonlinear Models • • • •
Artificial Neural Networks Nonlinear Regression Fuzzy Approach Bayesian Network Approach
Weekend
Week Days
Source-RTE France
Daily Consumption Second Peak First Peak
Afternoon Off Peak
Night Off Peak
Mainly Industrial Load
Residential + Commercial Load
Source-RTE France
Determining factors • Calendar Seasonal variation Daily variation Weekly Cycle Holidays • Economical or environmental • Weather Temperature Cloud cover or sunshine Humidity • Unforeseeable random event L(n) = f( past(L), Calendar, Weather,Other)
Why…. Neural Network? • Absence of the Mathematical Model of Load • The Load is function of a lot of factors L(n) = f (past(L), Calendar, Weather, Other)
• f is complex and unknown, and relation is non linear. • Traditional computationally economic approaches, such as regression and interpolation, may not give sufficient accurate result. Conversely, complex algorithmic methods with heavy computational burden can converge slowly and may diverge in certain cases, thus, not suitable for real time applications. • Use Black Box….i.e. Neural network to approximate f !
Major Impediments in Building ANN • Limited ability to extrapolate modelled relationship beyond the training data domain. • Results depend on the neural network design e.g. Number of the layers, Size of the hidden layer, Number of the inputs in the input layer etc. We do not have any clear information in this regard. • It is a Black Box…in the sense that the internal layers of the neural network are always opaque to the user, the mapping rules are thus difficult to understand.
Neural Network Architecture
Hidden layer
f
(∑
wk xk
)
Forecasted Load
Output layer
1 1 − e−x f ′ ( x ) = f ( x ) (1 − f ( x ) ) f (x) =
Input layer
STLF Using ANN (1st Approach)
LOAD IN MEGAWATT 4
8
Actual Load
x 10
7.5
MW
7
6.5
6
5.5 0
5
10
15 Hour of the day
Hour
23/12/2005
20/01/2006
00:00
70500
64200
01:00
67300
60800
02:00
68900
61700
03:00
66500
59000
04:00
64400
57100
05:00
64100
57000
06:00
66900
60300
07:00
71000
67400
08:00
76100
73000
09:00
77900
73000
10:00
79000
72700
11:00
78300
72100
12:00
78200
72100
13:00
78900
71900
14:00
76400
70400
data1
15:00
73800
68200
data2
16:00
71700
66700
17:00
72000
66300
18:00
76000
71000
19:00
77200
73700
20:00
74900
70500
21:00
72000
67100
22:00
69200
63400
23:00
72000
66400
20
Source: RTE France
25
Increment in MWatt
Com paris on of the load inc rem ent 8000 data1 data2
Increment in Load
6000
4000
2000
0
-2000
-4000
0
5
10 15 Hour of the Day
20
Source: RTE France
25
Hour
23/12/2005
20/01/2006
00:00
-3200
-3400
01:00
1600
900
02:00
-2400
-2700
03:00
-2100
-1900
04:00
-300
-100
05:00
2800
3300
06:00
4100
7100
07:00
5100
5600
08:00
1800
0
09:00
1100
-300
10:00
-700
-600
11:00
-100
0
12:00
700
-200
13:00
-2500
-1500
14:00
-2600
-2200
15:00
-2100
-1500
16:00
300
-400
17:00
4000
4700
18:00
1200
2700
19:00
-2300
-3200
20:00
-2900
-3400
21:00
-2800
-3700
22:00
2800
3000
23:00
-2000
-2411
STLF Using ANN (Proposed Approach)
Results (Using both Approaches)
Conclusion • The results obtained using the proposed approach are closer to the actual load, thus, strengthening the idea of proposed approach. • It was observed that the algorithm of the second approach was more robust as compared to the first approach. • It is less sensitive to the requirement of having training data representative of the entire spectrum of possible load and weather conditions.
STLF Using Fuzzy Neural Network
The input I11, I12 has five membership functions each. I11 represents the load increment at the kth hour and I12 represents the forecasted load increment at the same hour. The forecasted load increment was obtained using the traditional ANN.
Results
“The best way to predict the future is to invent it”
THANKS
Design and Simulation of Three Link Robot….(at Indian Space Research Satellite Centre, Bangalore, India)
•
Load flow study of a Nuclear Power Plant….(at Rajasthan Atomic Power Station, Kota, Rajasthan, India).
•
To Design and study Linear Induction Motor (LIM) for the Magnetic Levitated Vehicle….B.Tech Thesis work… (University Gold Medal for securing First Class First Position in the University). Desired O/P
•
Temperature Control System using ANN.
•
Short Term Load Forecasting Using Artificial Neural Network…M.Tech Thesis work.
•
Short Term Load Forecasting Using Fuzzy Neural Network….(follow up of the earlier work).
G-1
Desired O/P
G
SHORT TERM LOAD FORECASTING USING ANN
What’s Load Forecasting? • Tell the Future! • Short-term load forecasting (STLF) is for hour to hour forecasting and important to daily maintaining of power plant • A STLF forecaster calculates the estimated load for each hours of the day, the daily peak load, or the daily or weekly energy generation.
Taxonomy of Load Forecasting • Spatial forecasting : forecasting future load distribution in a special region, such as a state, a region, or the whole country. • Temporal forecasting is dealing with forecasting load for a specific supplier or collection of consumers in future hours, days, months, or even years.
Taxonomy of Load Forecasting (Cont) • Temporal forecasting: Long-term load forecasting (LTLF): mainly for system planning. Typically covers a period of 10 to 20 years. Medium-term load forecasting (MTLF): mainly for the scheduling of fuel supplies and maintenance. Usually covers a few weeks. Short-term load forecasting (STLF): for the daytoday operation and scheduling of the power system.
WHY Short-term load forecasting • An central problem in the operation and planning of electrical power generation. • To minimize the operating cost, electric supplier will use forecasted load to control the number of running generator unit. • STLF is important to supplier because they can use the forecasted load to control the number of generators in operation shut up some unit when forecasted load is low start up of new unit when forecasted load is high.
(HOW) Forecasting Methods • Expert Judgments • Linear Models • Linear Regression • Time Series Approach
• Nonlinear Models • • • •
Artificial Neural Networks Nonlinear Regression Fuzzy Approach Bayesian Network Approach
Weekend
Week Days
Source-RTE France
Daily Consumption Second Peak First Peak
Afternoon Off Peak
Night Off Peak
Mainly Industrial Load
Residential + Commercial Load
Source-RTE France
Determining factors • Calendar Seasonal variation Daily variation Weekly Cycle Holidays • Economical or environmental • Weather Temperature Cloud cover or sunshine Humidity • Unforeseeable random event L(n) = f( past(L), Calendar, Weather,Other)
Why…. Neural Network? • Absence of the Mathematical Model of Load • The Load is function of a lot of factors L(n) = f (past(L), Calendar, Weather, Other)
• f is complex and unknown, and relation is non linear. • Traditional computationally economic approaches, such as regression and interpolation, may not give sufficient accurate result. Conversely, complex algorithmic methods with heavy computational burden can converge slowly and may diverge in certain cases, thus, not suitable for real time applications. • Use Black Box….i.e. Neural network to approximate f !
Major Impediments in Building ANN • Limited ability to extrapolate modelled relationship beyond the training data domain. • Results depend on the neural network design e.g. Number of the layers, Size of the hidden layer, Number of the inputs in the input layer etc. We do not have any clear information in this regard. • It is a Black Box…in the sense that the internal layers of the neural network are always opaque to the user, the mapping rules are thus difficult to understand.
Neural Network Architecture
Hidden layer
f
(∑
wk xk
)
Forecasted Load
Output layer
1 1 − e−x f ′ ( x ) = f ( x ) (1 − f ( x ) ) f (x) =
Input layer
STLF Using ANN (1st Approach)
LOAD IN MEGAWATT 4
8
Actual Load
x 10
7.5
MW
7
6.5
6
5.5 0
5
10
15 Hour of the day
Hour
23/12/2005
20/01/2006
00:00
70500
64200
01:00
67300
60800
02:00
68900
61700
03:00
66500
59000
04:00
64400
57100
05:00
64100
57000
06:00
66900
60300
07:00
71000
67400
08:00
76100
73000
09:00
77900
73000
10:00
79000
72700
11:00
78300
72100
12:00
78200
72100
13:00
78900
71900
14:00
76400
70400
data1
15:00
73800
68200
data2
16:00
71700
66700
17:00
72000
66300
18:00
76000
71000
19:00
77200
73700
20:00
74900
70500
21:00
72000
67100
22:00
69200
63400
23:00
72000
66400
20
Source: RTE France
25
Increment in MWatt
Com paris on of the load inc rem ent 8000 data1 data2
Increment in Load
6000
4000
2000
0
-2000
-4000
0
5
10 15 Hour of the Day
20
Source: RTE France
25
Hour
23/12/2005
20/01/2006
00:00
-3200
-3400
01:00
1600
900
02:00
-2400
-2700
03:00
-2100
-1900
04:00
-300
-100
05:00
2800
3300
06:00
4100
7100
07:00
5100
5600
08:00
1800
0
09:00
1100
-300
10:00
-700
-600
11:00
-100
0
12:00
700
-200
13:00
-2500
-1500
14:00
-2600
-2200
15:00
-2100
-1500
16:00
300
-400
17:00
4000
4700
18:00
1200
2700
19:00
-2300
-3200
20:00
-2900
-3400
21:00
-2800
-3700
22:00
2800
3000
23:00
-2000
-2411
STLF Using ANN (Proposed Approach)
Results (Using both Approaches)
Conclusion • The results obtained using the proposed approach are closer to the actual load, thus, strengthening the idea of proposed approach. • It was observed that the algorithm of the second approach was more robust as compared to the first approach. • It is less sensitive to the requirement of having training data representative of the entire spectrum of possible load and weather conditions.
STLF Using Fuzzy Neural Network
The input I11, I12 has five membership functions each. I11 represents the load increment at the kth hour and I12 represents the forecasted load increment at the same hour. The forecasted load increment was obtained using the traditional ANN.
Results
“The best way to predict the future is to invent it”
THANKS
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