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My activities in ENEA. Summer 2009.

My activities in ENEA. Summer 2009.

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  • ENEA deals with several application fields, from material science to biotechnlogies from nuclear energy to renewable energy.

    In the Research Centre where I work, 20km from Rome, there are two working nuclear reactors and a solar plant of the Archimedes Project developed by the nobel prize Carlo Rubbia
  • ENEA deals with several application fields, from material science to biotechnlogies from nuclear energy to renewable energy.

    In the Research Centre where I work, 20km from Rome, there are two working nuclear reactors and a solar plant of the Archimedes Project developed by the nobel prize Carlo Rubbia
  • ENEA deals with several application fields, from material science to biotechnlogies from nuclear energy to renewable energy.

    In the Research Centre where I work, 20km from Rome, there are two working nuclear reactors and a solar plant of the Archimedes Project developed by the nobel prize Carlo Rubbia
  • I work in the department of Renewable Energy Sources and Energy Saving Department
  • I work in the department of Renewable Energy Sources and Energy Saving Department
  • I work in the department of Renewable Energy Sources and Energy Saving Department
  • I work in the department of Renewable Energy Sources and Energy Saving Department
  • I work in the department of Renewable Energy Sources and Energy Saving Department
  • Since about 2 years our work focused on building efficiency.
    An eco-building is a building designed with particular regard to energy consumption and polluting emissions and then often these building produce their own energy with solar panels, micro-turbines, geothermal heat pumps and so on...
    Ecobuildings can sell their surplus energy to the electric grid
  • Since about 2 years our work focused on building efficiency.
    An eco-building is a building designed with particular regard to energy consumption and polluting emissions and then often these building produce their own energy with solar panels, micro-turbines, geothermal heat pumps and so on...
    Ecobuildings can sell their surplus energy to the electric grid
  • Since about 2 years our work focused on building efficiency.
    An eco-building is a building designed with particular regard to energy consumption and polluting emissions and then often these building produce their own energy with solar panels, micro-turbines, geothermal heat pumps and so on...
    Ecobuildings can sell their surplus energy to the electric grid
  • Since about 2 years our work focused on building efficiency.
    An eco-building is a building designed with particular regard to energy consumption and polluting emissions and then often these building produce their own energy with solar panels, micro-turbines, geothermal heat pumps and so on...
    Ecobuildings can sell their surplus energy to the electric grid
  • These three are the activities I’m currently involved in.
    These activities are in collaboration with different italian universities, companies and Italian Economic Development Office.
  • These three are the activities I’m currently involved in.
    These activities are in collaboration with different italian universities, companies and Italian Economic Development Office.
  • These three are the activities I’m currently involved in.
    These activities are in collaboration with different italian universities, companies and Italian Economic Development Office.
  • Classical design approach maybe doesn’t permit to obtain the optimization we need for eco-buildings. Because besides costs we have to consider other parameters like these three.
    Unlike classical buildings we need to optimize the energy sources
  • Classical design approach maybe doesn’t permit to obtain the optimization we need for eco-buildings. Because besides costs we have to consider other parameters like these three.
    Unlike classical buildings we need to optimize the energy sources
  • Classical design approach maybe doesn’t permit to obtain the optimization we need for eco-buildings. Because besides costs we have to consider other parameters like these three.
    Unlike classical buildings we need to optimize the energy sources
  • Classical design approach maybe doesn’t permit to obtain the optimization we need for eco-buildings. Because besides costs we have to consider other parameters like these three.
    Unlike classical buildings we need to optimize the energy sources
  • Classical design approach maybe doesn’t permit to obtain the optimization we need for eco-buildings. Because besides costs we have to consider other parameters like these three.
    Unlike classical buildings we need to optimize the energy sources
  • Classical design approach maybe doesn’t permit to obtain the optimization we need for eco-buildings. Because besides costs we have to consider other parameters like these three.
    Unlike classical buildings we need to optimize the energy sources
  • Classical design approach maybe doesn’t permit to obtain the optimization we need for eco-buildings. Because besides costs we have to consider other parameters like these three.
    Unlike classical buildings we need to optimize the energy sources
  • Classical design approach maybe doesn’t permit to obtain the optimization we need for eco-buildings. Because besides costs we have to consider other parameters like these three.
    Unlike classical buildings we need to optimize the energy sources
  • There are different types of software simulator, we are developing a dynamic simulator MATLAB based called ODESSE (Optimal Design for Smart Energy) which will be able in the future to simulate the so-called energy districts.

    Probably this diagram misses of a piece...
    software needs the knowledge of the national and international laws about standards and restrictions about emissions, temperature, materials and so on...
  • A MA is an evolutionary algorithm with a local improvement procedure, like a local search.
  • Currently we work with a single-objective approach but we’re studying a MO approach.

    The ED simulation is quite challenging: need to manage the energy policies of the building, an online optimization of the costs because the price of energy varying during the day...
  • Currently we work with a single-objective approach but we’re studying a MO approach.

    The ED simulation is quite challenging: need to manage the energy policies of the building, an online optimization of the costs because the price of energy varying during the day...
  • Currently we work with a single-objective approach but we’re studying a MO approach.

    The ED simulation is quite challenging: need to manage the energy policies of the building, an online optimization of the costs because the price of energy varying during the day...
  • The second activity is about the modeling of environmental parameters for simulating purpose.

    We obtained good results with temperature and in few months we want to start with ambient humidity modeling, we’re currently collecting data
  • Soft computing approaches are often used
    (survey?)

    We must work with official data, data which comes from government database.

    Climate is non-linear, a place 2km far from here could be show a different temperature profile, because temperature depends on mountains, wind, solar exposure and so on...
  • Soft computing approaches are often used
    (survey?)

    We must work with official data, data which comes from government database.

    Climate is non-linear, a place 2km far from here could be show a different temperature profile, because temperature depends on mountains, wind, solar exposure and so on...
  • Soft computing approaches are often used
    (survey?)

    We must work with official data, data which comes from government database.

    Climate is non-linear, a place 2km far from here could be show a different temperature profile, because temperature depends on mountains, wind, solar exposure and so on...
  • TRNSYS (pronounced 'tran-sis'), commercially available since 1975, is a flexible tool designed to simulate the transient performance of thermal energy systems
  • NNs are a common tool for modeling problems
  • we weren’t satisfied of BP performance

Transcript

  • 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