1. New Advanced Technology Methods on Energy Efficiency of Buildings
8th International Scientific Conference on “Energy and Climate Change”
Session: Energy and Climate Change
New Advanced Technology Methods on Energy Efficiency of Buildings
Peter P. Groumpos, Vassiliki Mpelogianni
Laboratory of Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras

2. Presentation Outline Definition of the problem Intelligent Buildings
Building Energy Management Systems
Conventional Versus Fuzzy Control
Fuzzy Cognitive Maps
Theory
Simulation
Conclusions
Future Research

3. Problem Definition
Aim of the study: Energy efficiency achievement through the reduction of the energy consumption of a building.
Question: How can this be achieved, and which is the most suitable method?

4. What is an Intelligent Building
Categories of definitions:
Performance based definitions
Service based definitions
System based definitions
Heating
Ventilation
Air-Conditioning
Lighting,
Fire Security
Data
Access Control
Central
Unit

5. Building Energy Management Systems (BEMS)
Part of the Building Automation System
Used to control the Heating Cooling and Hot Water production, in order to reduce the energy costs of a building.
It is also used for measuring the building’s energy consumption, optimizing system running strategy and improving system management
Along with other parts of the building’s automation such as lighting control the BEMS help satisfy the residents needs and comfort when on the same time it achieve notable reductions in energy consumption.

6. Conventional versus Fuzzy Control
Conventional Control = Control through mathematical modelling
Fuzzy Control= Control with the use of linguistic variables, approaching the human way of thinking.
Comparison of control loop behavior for the optimization of an HVAC system, between a Direct Digital and a Fuzzy Logic Controller. The use of fuzzy logic needs less control time than Direct Digital control. The big overshoot is a reason of large control time (Tc) in Digital controller, with the use of specific rules of fuzzy controller, process variables are brought to the new set point to avoid processing variable’s overshooting.

7. Control Methods Digital Control PLC Control Fuzzy Logic
Artificial Neural Networks
Fuzzy cognitive Maps

8. Fuzzy Cognitive Maps (1/3)
Graphical representation describing the cause and effect between nodes
Incorporation of the accumulated knowledge and experience of experts
FCMs consist of:
concept nodes: key-factors and characteristics of the modeled complex system and stand for: events, goals, inputs, outputs, states, variables and trends of the complex system been modeled
weighted arcs: connect the concept nodes, represent the causal relationship that exists among concepts

9. Fuzzy Cognitive Maps (2/3)
Causal interrelationships
The value of Wij indicates how strongly concept Ci influences concept Cj
The sign of Wij indicates whether the relationship between concepts Ci and Cj is direct or inverse
The direction of causality indicates whether concept Ci causes concept Cj, or vice versa.

10. Fuzzy Cognitive Maps (3/3)
Mathematical representation of FCMs
Generic formulation for calculating the values of concepts at each time step
k1: expresses the influence of the interconnected concepts in the configuration of the new value of the concept Ai
k2 represents the proportion of the contribution of the previous value of the concept in the computation of the new value
f : threshold function and to squash the result in the interval [0,1]

11. Learning on Fuzzy Cognitive Maps
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.

12. Why use 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.

13. BEMS Model The BEMS consists of: A boiler for DHW storage.
A storage tank for the storage of water for the heating and cooling.
A PV-T Unit for the production of electricity and hot water.
A Heat Pump (air to water) for heating and air conditioning.
A Floor Heating unit which can reduce the fuel needed by 30%.
A Fan Coil Unit (FCU) to cover the rest of the cooling and heating needs.
Four circulators and triode valves are used to distribute the water to the various parts of the automation.

14. Fuzzy Cognitive Maps Modeling (1/2)
CONCEPTS:
C10: Valve 2a
Inputs
C11: Valve 2b
C1:PV-T temperature
C12: Valve 3
C13: Valve 4
C2:FCU temperature
C14: Circulator 1
C3:Floor Heating Temperature
C15: Circulator 2
C4:DHW Demand
C16: Circulator 3
C5:Contamination Flag
C17: Circulator 4
Medium Outputs
Final Outputs
C6:Storage Tank Temperature
C18: Resistor Operation
C19: Heat Pump Operation
C7: Boiler Temperature
C8:Valve 1a
C9: Valve 1b

15. Fuzzy Cognitive Maps Modeling (2/2)

16. Conclusions (1/2)
To meet Intelligent Buildings goals, using control strategies is unavoidable.
Intelligent control systems are more developed for improving the energy efficiency of buildings.
The combination of control techniques of fuzzy logic and neural networks is recommended to control buildings.
The characteristics and simplicity of the mathematical model of FCMs, show that implementing the FCMs as a direct control is recommended in controlling the parameters of a building’s automation to achieve more intelligence in building as well as more energy saving.

17. Conclusions (2/2)
The run time of control process is declined, and due to the ability of FCMs in having learning strategy, the consumption of energy could be decreased. 
With the use of FCMs we do not need the rules; except for those required for the definition of the initial values, so the algorithm can more easily implemented into the system .

18. Future Research
The creation of a system in order to ensure the energy independence of a building and the efficient and economic use of its resources while taking advantage of the renewable energy sources.
Data that can and will be used in future research of moving from high energy consumption to Zero Energy Buildings (ZEB).

19. TOGETHER WE CAN DO IT DIVIDED WE WILL FAIL Our Attempt:??!!
To Build Wise Systems Of Ourselves
TOGETHER WE CAN DO IT
DIVIDED WE WILL FAIL
CRITICS TO OUR DICISIONS AND ACTIONS
WILL BE THE UNBORN DEADS
A Greek Poet

20. NO MAN IS WISE ENOUGH BY HIMSELF (Plato)
A MAN SHOULD DEVOTE EIGHT(8) HOUR TO SLEEP, EIGHT(8) HOURS TO WORK AND EIGHT(8) HOURS TO HIS MIND (Aristotle)
OUR VISION TO THE WORLD OF 2050???!!!
EXPECT:
FEED 9-10 BILLION PEOPLE
MANAGE CLIMATE CHANGE
SECURE ENERGY SYPPLY TO ALL COUNTIES
PROVIDE FOOD (SAFE??!!) TO EVERYBODY
WORLD TO BE:
BETTER EDUCATED
MORE INNOVATIVE,
HEALTHIER
RICHER
MORE SUSTAINABLE
MORE SECURE
LESS INEQALITY BETWEEN RICH AND POOR AND BETWEEN
MAN AND WOMEN
QUESTION: ARE WE HAPPY WITH ALL THE ABOVE??

21. Thank you for your attention! Questions?
Peter P. Groumpos
Vassiliki Mpelogianni
Laboratory of Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras