My activities in ENEA. Summer 2009.

1. Soft computing approaches
for energy related problems
Matteo De Felice
<matteo.defelice@enea.it>

2. ENEA

3. ENEA
Italian Energy, New Technologies and Environment Agency

4. ENEA
Italian Energy, New Technologies and Environment Agency
ENEA’s mission is to support country’s competitiveness and
sustainable development

5. ENEA
Italian Energy, New Technologies and Environment Agency
ENEA’s mission is to support country’s competitiveness and
sustainable development
3.000 employers in 13 research centers in Italy

6. ENEA main topics

7. ENEA main topics
Environment and Sustainable Development

8. ENEA main topics
Environment and Sustainable Development
Biotechnology

9. ENEA main topics
Environment and Sustainable Development
Biotechnology
Nuclear energy

10. ENEA main topics
Environment and Sustainable Development
Biotechnology
Nuclear energy
New Materials

11. ENEA main topics
Environment and Sustainable Development
Biotechnology
Nuclear energy
New Materials
Energy Efficiency and Renewable Energies

12. Eco-buildings

13. Eco-buildings
Sustainable buildings
produce and use energy in a
more effective way

14. Eco-buildings
Sustainable buildings
produce and use energy in a
more effective way
less CO2 emissions for heating

15. Eco-buildings
Sustainable buildings
produce and use energy in a
more effective way
less CO2 emissions for heating
less primary energy
consumption

16. Eco-buildings
Sustainable buildings
produce and use energy in a
more effective way
less CO2 emissions for heating
less primary energy
consumption
better thermal comfort

17. Activities

18. Activities
Optimal design of eco-buildings

19. Activities
Optimal design of eco-buildings
Estimation of environmental parameters

20. Activities
Optimal design of eco-buildings
Estimation of environmental parameters
Energy consumption forecasting for Italian regions

21. Optimal design of eco-buildings

22. Optimal design of eco-buildings
In order to optimize:

23. Optimal design of eco-buildings
In order to optimize:
energy consumptions

24. Optimal design of eco-buildings
In order to optimize:
energy consumptions
pollutant emissions

25. Optimal design of eco-buildings
In order to optimize:
energy consumptions
pollutant emissions
thermal comfort

26. Optimal design of eco-buildings
In order to optimize:
energy consumptions
pollutant emissions
thermal comfort
the selection of:

27. Optimal design of eco-buildings
In order to optimize:
energy consumptions
pollutant emissions
thermal comfort
the selection of:
generators typologies (solar panels, micro-turbines, etc etc)

28. Optimal design of eco-buildings
In order to optimize:
energy consumptions
pollutant emissions
thermal comfort
the selection of:
generators typologies (solar panels, micro-turbines, etc etc)
energy generators size and parameters

29. Optimal design of eco-buildings
In order to optimize:
energy consumptions
pollutant emissions
thermal comfort
the selection of:
generators typologies (solar panels, micro-turbines, etc etc)
energy generators size and parameters
is crucial

30. Optimal design
generators
parameters
software
performance
simulator
building data

31. Our Approach

32. Our Approach
Fitness evaluation is computationally expensive (from 30
minutes to 2 hours!)

33. Our Approach
Fitness evaluation is computationally expensive (from 30
minutes to 2 hours!)
A Memetic Algorithm with Fitness Approximation approach
was used

34. Memetic Algorithms with Fitness
Approximation
Memetic Algorithm using an
approximated fitness function
The best solution is evaluated with
the real fitness function
Approximated fitness function is
updated

35. Future Works

36. Future Works
Multi-Objective Approach

37. Future Works
Multi-Objective Approach
In-depth examination of Fitness Approximation
methodologies

38. Future Works
Multi-Objective Approach
In-depth examination of Fitness Approximation
methodologies
Energy District Simulation

39. Climatic features modeling with soft
computing methodologies
(Eco)buildings software
simulators need an estimation of
environmental parameters as:
Solar radiation
Ambient temperature
Ambient humidity

40. Temperature data

41. Temperature data
Existing data for few places

42. Temperature data
Existing data for few places
Data based on monthly averages

43. Temperature data
Existing data for few places
Data based on monthly averages
Climate is an highly non-linear systems: locations close to
each other have often different temperature profiles

44. Nearest-Neighbour
Simplest and common approach (used in commercial
software like TRNSYS)
t = ti where ti is the nearest known point in the database

45. Neural networks
geographical
coordinates
temperature
ANN (°C)
day of the year

46. Neural networks training

47. Neural networks training
We compared different training methods:

48. Neural networks training
We compared different training methods:
Back-propagation (BP)

49. Neural networks training
We compared different training methods:
Back-propagation (BP)
Genetic Algorithms (GA)

50. Neural networks training
We compared different training methods:
Back-propagation (BP)
Genetic Algorithms (GA)
BP-GA Hybrid

51. BP-GA Approach
Supportive combination
5% of the initial population of GA is trained with BP

52. Validation
Validate on 740 Italian
localities subdivided in
different areas

53. Results
5
4.99
3.75
3.78
2.5 The BP-GA approach leads to an
2.39
2.16 2.2 estimation more accurate than
1.25 1.3
1.12
classical approach - in the worst
0.86
0
0.62 0.7
case the difference was of 3500
Nearest-N. BP GA BPGA SVM kWh on a year simulation
Avg error (°C)
Max error (°C)

54. Next step...
Ambient humidity modeling: humidity is crucial for the
calculation of latent heat

55. Regional energy consumption forecasting
Forecasting of yearly energy
consumption data of different
sectors
Italian regions need updated
estimations of energy
consumptions for their Strategic
Plans

56. Energy consumptions
Energy consumption (ktoe) from different sources: coke,
coal, gas, electricity etc etc
20,000
15,000
10,000
5,000
0

57. Data Features
Use economic indicators as added value, index of industrial
production, fixed investments, oil price etc etc
Data available for each year from 1971: only 37 points
Not enough data to capture the dynamics!

58. Forecasting
22 variables (for 20 regions!)
10 economic indicators
Goal: 2-years forecast

59. To-Do List
Analysis of data: clustering, correlation coefficients
Dimensional analysis: PCA
Selection of a time-series forecasting model (regressive
model? black-box approach like NARX model? Neural
networks? Support Vector Regression?)
Validation on real data

60. Correlation Coefficients
1
0.75
0.5
0.25
0
-0.25
Electricity
-0.5
Fixed Investments
-0.75
Production
-1
Elect. F.I. Prod.
Elect. 1 0.982 0.982 Pearson
F.I. 0.982 1 0.996 correlation
Prod. 0.982 0.996 1 coefficient

61. Correlation Coefficients (WEKA)

62. ARX & NARX Models
ARX model is a linear difference equation:
y(t) = a1 y(t − 1) + . . . + ana y(t − na ) + b1 u(t − 1) + . . . + bnb u(t − nb )
NARX model is the non-linear equivalent:
y(t) = f (y(t − 1), . . . , y(t − na ), u(t − 1), . . . , u(t − nb ))
f it could be a neural network

63. Models
Regressive linear models: AR, MA, ARMA, ARIMA etc etc
Non-linear models: LSTAR, bilinear model
Neural Networks
Fuzzy-based approach
Hybrid approach

64. to be continued...

65. Publications
Ceravolo F., De Felice M. , Pizzuti S. : "Combining Back-Propagation and Genetic Algorithms to Train Neural Networks
for Ambient Temperature Modeling in Italy", EvoWorkshops 09, Tuebingen (Germany), April 2009 (in Springer
Applications of Evolutionary Computing. Lecture Notes in Computer Science)
Ceravolo F., De Felice M. , Pizzuti S. : "Ambient Temperature Modeling through Traditional and Soft Computing
Methods", HAIS08, Burgos (Spain), sept. 2008 (in Springer Lecture Notes in Artificial Intelligence)
Ceravolo F. , Di Pietra B. , Pizzuti S. , Puglisi G. "Neural models for ambient temperature modeling", IEEE-CIMSA08,
Istanbul (Turkey), July 2008