Fuzzy Cognitive Maps and Zero Energy Buildings Technologies

1. 8th International Scientific Conference on Energy and Climate Change Fuzzy Cognitive Maps and Zero Energy Buildings Technologies Peter P. Groumpos Professor, Electrical and Computer Engineering Department University of Patras Eleni S. Vergini PhD Canditate, Electrical and Computer Engineering Department University of Patras

2. Outline • Introduction • General Information on Zero Energy Buildings (ZEB) • Fuzzy Cognitive Maps (FCM) • Modeling ZEB using FCM • Spring and Autumn Simulations • Conclusions

3. Introduction • Buildings consume 30-40% of all primary energy, and produce 36% of CO2 emissions • EU: Energy Performance of Buildings Directive (EPBD) in 2010 and Energy Efficiency Directive in 2012 • USA: US Department of Energy (DOE) with the Building Technologies Program • 20-20-20: • 20% Energy Saving • 20% Renewable Energy Sources • 20% Carbon Emission Reduction • By the end of 2020 all new buildings will have to be nearly Zero Energy Buildings • It is not easy to establish a specific approach due to different policies and approaches in different countries.

4. Zero Energy Building (1/3) • Produces, within its boundaries, as much energy as it consumes, on an annual basis • Renewable energy sources, located on/near the building with reasonable cost • Absence of specific characteristics and equipment requirements • Aim • Balance Achievement • Design Methodology • Mathematical Model

5. Zero Energy Building (2/3) • Categories: • Autonomous or Stand-alone • Nearly Zero Energy Building (nZEB) • Net Zero Energy Building (NZEB) • Net-Plus or Positive Energy Building • Classification according to the Energy Supply • Energy Source • Site of Energy Production • Energy Costs • Emissions

6. Zero Energy Building (3/3) • Specific Comfort Conditions • Modeling and Control Parameters • Size and construction materials • Utility (commercial, public, residential) • Geographical position • Regional climate levels or “microclimate” • Available renewable energy sources • Unpredicted parameters • Traditional Control Methods → Separation of Tasks • Intelligent Systems → Groups of Applications The Graphic Comfort Zone Method, Standard 55-2010. Source ASHRAE

7. Modelling complex Systems Using Fuzzy Cognitive Maps (FCMs) • Modeling a system as a collection of concepts and causal links between them. • Nodes: Represent the system’s concepts. Concepts correspond to the characteristics of the system. • Arrows:Interconnection between nodes. Show the cause-effect relationship between them.

8. Fuzzy Cognitive Maps (1/3) Between concepts, there are three possible types of causal relationships that express the type of influence from one concept to another: • Wij > 0 (Ci ↑ ⇒ Cj ↑) • Wij < 0 (Ci ↑ ⇒ Cj ↓) • Wij = 0 (Ci , Cj⇒ not correlated) Attention: Causality vs. Correlation

9. Fuzzy Cognitive Maps (2/3) • The FCM concepts take initial values. These values change, depending on the interaction, until they reach an equilibrium • The value of each concept is calculated applying the following calculation rule at each simulation step : Where f is the sigmoid function (λ>0 steepness of the function) • The iterations stop when a stable state is achieved

10. Fuzzy Cognitive Maps (3/3) Training methods for the weights (Wij): • Active Hebbian Learning algorithm • Nonlinear Hebbian Learning algorithm • Evolutionary algorithms • Experts exclusion algorithm Basic concept of the abovementioned methods is the minimization of specific criteria functions in order to control the desired output region of the system.

11. Why Fuzzy Cognitive Maps?? There are three main reasons that require the utilization of Fuzzy Cognitive Maps (FCMs): • Complexity • Nonlinearities • Uncertainty The majority of the real world systems include these three parameters. The conventional control methods for such systems cannot confront these parameters as the FCMs do. Thus, FCMs are about to play a major role in the future regarding the modeling, analysis, and control of complex systems.

12. Intelligent Zero Energy Building • Low energy consumption • Lower impact on the environment. • Improved comfort conditions for hosted people • Increased productivity of stuff • Longer lifecycle of building and its equipment • Reduced system and equipment failures • Reduced cost of operation and maintenance.

13. FCM application on ZEB (1/3) • C1: PV System • C2: Wind Turbine • C3: Lighting • C4:Electrical/Electronic Devices • C5:Heating • C6:Cooling • C7:Solar Radiation • C8: Wind Velocity • C9: Windows

14. FCM application on ZEB (2/3) • C10: Natural Light inside the building • C11:Shading • C12: Internal Temperature • C13:External Temperature • C14:Geothermal Energy • C15: Total Produced Energy • C16: Total Consumed Energy

15. FCM application on ZEB (3/3)

16. Spring (1/3)

17. Spring (2/3)

18. Spring (3/3) • In above diagram, the higher curve represents the total production and the bottom curve is the total consumption. It is assumed that during spring the building can satisfy its own energy needs and have a positive energy balance. A positive balance is reasonable in the case of spring, since the climate in Greece is rather sunny and without extreme temperatures. Those conditions offer a satisfactory amount of energy production and do not require energy consumption for heating or cooling. As a consequence the consumed energy is not very high.

19. Autumn (1/3)

20. Autumn (2/3)

21. Autumn (3/3) • The higher curve represents the total energy production and the bottom curve represents the total energy consumption. As in the case of spring, from the autumn diagram it is assumed that the building can also satisfy its own energy requirements during autumn.

22. Conclusions • Intelligent Systems are necessary in the case of ZEB, since it is a complex system with many parameters. • FCMs, based on human thought and reasoning, make that complex problem seem rather simple. • All the single parts of the building and their interaction are considered at the same time. • A specific methodology of energy management and balance achievement has not yet been established. • Mathematical modeling of ZEB and creation of calculation and design methodologies.

23. Future Research • Further development of the FCM methods to address the control of buildings. • Development of software tools for specific applications on Power systems (energy efficiency, energy management, load management, BEMs, controls, etc). • Technoeconomic studies for energy systems. • Design future ZEBs using FCMs.

24. Thank you for your attention! groumpos@ece.upatras.gr vergini@ece.upatras.gr