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ScienceDirect Procedia Technology 11 (2013) 99 – 106

The 4th International Conference on Electrical Engineering and Informatics (ICEEI 2013)

Intelligent Optimized Control System for Energy and Comfort Management in Efficient and Sustainable Buildings Pervez Hameed Shaikh*, Nursyarizal Mohd. Nor, Perumal Nallagownden, Irraivan Elamvazuthi Universiti Teknologi Petronas, Department of Electrical and Electronics Engineering, Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia

Abstract Building energy efficiency and management provides remarkable automation opportunities, which fulfills dwellers comfort index. The challenging issue of the building envelope is to save energy and achieve high comfortable environment simultaneously. In this study, control system behavioral model with its framework has been developed for smart buildings. The power consumption of the actuator system and comfort index are two control optimization objectives in the system design. Two functions utilize Multi-objective Genetic Algorithm (MOGA) to generate Pareto front, obtained from the Pareto optimal solutions for multi-dimensional problem. The acquired non-dominated solutions are significant for building energy and comfort management in informed decision-making. © byby Elsevier B.V. © 2013 2013The TheAuthors.Published Authors. Published Elsevier Ltd. Selection ofof Information Science and&Technology, Selectionand andpeer-review peer-reviewunder underresponsibility responsibilityofofthe theFaculty Faculty Information Science Technology,UniversitiKebangsaan Universiti Kebangsaan Malaysia. Malaysia. Keywords: Energy efficiency; Energy management; Smart building envelopes; Optimization; Comfort management; Multiobjective.

1. Introduction The notion of energy efficiency is important not only because it favors stable economy but to tackle climate change, progressive reduction of fossil fuels and develop awareness to reduce energy consumption. The efficient use of energy resources in building can provide decreased energy exploitation and manage desired comfort.

* Corresponding author. Tel.: +060-14-9971034. E-mail address:[email protected]

2212-0173 © 2013 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia. doi:10.1016/j.protcy.2013.12.167

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Buildings consume about 40 % of the primary energy supplied [1- 3] since, there is the growing concern about building energy consumption with rising comfort requirements. The multi-dimensional conflicting aspect of building is to save energy and the achievement of indoor comfort conditions. Ensuring comfort conditions in building is important due to 90% of the world’s population spend most of the time in buildings [4, 5]. These have a direct impact on dwellers productivity, health and efficiency and pose indirect impact on energy efficiency of buildings. Therefore, to make use of limited energy resources and to fulfill the occupants’ comfort demands, an intelligent control system is intended. The energy management of intelligent buildings is significant, since it contributes to the continuous management and thus saving energy and cost ultimately maintaining comfort. Active systems are generally being controlled, that is heating, cooling and ventilation (HVAC) systems, through building energy management system (BEMS) [5, 6]. Generally, ambient temperature in buildings indicates thermal comfort, whereas auxiliary heating and cooling system is applied to maintain the temperature in comfortable region. Several building energy management systems have been developed and a number of studies conducted in [7-9] for modern intelligent control systems for buildings. Thus, revealing the ongoing interest of researchers and scientists to explore the balance (i.e trade-off) between the energy consumption and the comfort level. This paper reports the behavioral model relationship between energy consumption and thermal comfort. The two objective functions of building automation are power consumption and comfort. A meta-heuristicoptimization algorithm termed Multi Objective Genetic Algorithm (MOGA) is employed to optimize building control system. 2. Control System Framework and Model The Genetic Algorithm tunes and optimizes the set points of the indoor thermal information and the user preference range. Various occupants have set different comfort preference range. The fuzzy control is employed to compute the power demand in order to maintain the desired comfort level, controlling the actuator subsystem. The difference in the sensed value and set points are inputs, employed in the fuzzy inference control engine. The required power will be matched to the power availability of the system. The comfort level will be adjusted according to the power supplied to the system through the central control coordinator as determined in Fig 1.

Power Adjusted through the central control Optimizer Parameter Comfort Range

Consumer Set Point

∑

FIS Knowledge Base Fuzzy Expert System

Comparator

Level Sensor Feedback

Power Demand

Fig. 1. Framework model proposed for automated building.

Automated Building

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2.1. Fuzzy Control System The distributed local fuzzy control agent is utilized to determine the required power for a thermal actuator system to activate and maintain indoor environmental comfort. The inputs for the controller is the error (between the measured values and optimized set values) and the error difference (described between prior and present value), and the output of the required power are shown in Fig. 2. The proportional derivative fuzzy control rule base is employed as shown in table 1. The inputs provide the fuzzy model considering the thermal indoor comfort range with the standard interval range [10]. The fuzzy output is applied to the curve fit to drive the robust model for the power demand as determined in Eq. 1 with 97 % statistical accuracy along with the graph in Fig 3.

PTemp (t ) 5.655* ETemp (t ) 2.961

(1)

where, PTemp is the power required for the temperature actuator ETemp is the error between the sensor and set point value Table 1: Fuzzy PD rule base control constraints Power Required

EDTemp

NL NA NS NE PS PA PL

NL NL NL NL NL NL NL NL

NA NS NA NA NA NL NL NL

NS PS NE NS NS NA NA NL

ETemp NE PL PA PS NE NS NA NL

PS PL PA PA PS PS NE NS

PA PL PL PL PA PA PA PS

PL PA PA PA PA PA PA PA

2.2. Problem Formulation The conflict between the power consumption and user comfort is a challenging task to tackle in the building indoor environment. Thus, two conflicting objective function issues can be solved with Pareto-optimal Front. This provides increased flexibility and options to the ultimate customers making the most of the available resources. The decision variable vector is set as the target temperature ‘T’ for designing the objective function of comfort and power consumption. The power consumption and comfort function with respect to the variable control vector are described as, Objective functions; Minimize f1(x) and f2(x)

(2)

Discomfort = 1 - Comfort

(3)

T Tset 2 f1 ( x) 1 ( ) Tset Power Consumption = PTemp

f 2 ( x) 5.655*T 2.961

(4)

5.655*T 2.961

(5) (6)

wherePTemp is the power required for targeted environmental parameter to drive. The function (1) has been utilized from [11, 12] whereas, the function (3) obtained from the curve fitting model. The thermal comfort set points taken from ASHARE standard, which is employed in fuzzy controller with linguist fuzzy membership set for inputs and

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outputs. The input data to the fuzzy system is the primary range stochastic interval provided by the environmental protection agency (EPA) [10]

PTemp.

Fig. 2. Fuzzy input and output membership function

ETemp Fig. 3. Curve fit model for power consumption

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2.3. Multiobjective Genetic Algorithm (MOGA) Genetic algorithm (GA) is a concentrating search technique, mimics the biological processes to perform a random search in a defined N-dimensional possible set of solutions [13]. John Holland at University of Michigan initially discovered it in the 1970s. The main idea is to design artificial systems retaining the robustness and adoption properties of natural systems. In optimization problems, it is needed to find the best solution in a given search space. The GA utilizes objective functions rather the need of derivative and other auxiliary information. It implements probabilistic rules and parallel search in population proceeds towards optimum solutions using genetic operators [14]. In the multiobjective optimization problems, instantaneously two or more objective functions are optimized and usually are in conflict with each other. In consequence, no unique solution is obtained but instead it aimed to find all best possible trade-off solutions available called Pareto optimal set. The general mathematical representation of multiobjective optimization problem is determined as, (7) Minimize F ( x) [ f1 ( x), f 2 ( x),.... f k ( x)] Subject to m inequality constraints;

gi ( x) d 0

i 1, 2,...m

and p equality constraints;

hi ( x) 0

i 1, 2,... p

We wish to determine from the set

(9)

fi : o . x [ x1 , x2 ,...xn ] is the vector of decision variables. n

where, k is the number of objective functions

(8) T

' F ' of all vectors, satisfying equations (7) and (8). The corresponding vectors

*

x yield optimum values of all objective functions are called non-dominated. In this research GA is employed in tune the consumer preference set points according to the outdoor environmental information. Since, various customers have different preferences for their comfort zone requirement as set point plays an important role in control targets. The objective functions defined in equation (4) and (6) and the optimization goal is to minimize. Thus, the error between the measured and set values determines the inhabitants comfort level and power consumption. 3. Results and discussion In this simulation, the initial temperature is set at 60 oF and the set point for index comfort requirement is 70 oF. If all these set points are entirely maintained the overall comfort value will be as high as ‘1’. Thus employing a Pareto based approach, the lower and upper bounds are selected as 68 oF and 78 oF respectively which serves as constraints and population size is set at 300. Using MOGA algorithm, the pareto front of the comfort versus the energy consumption is generated as shown in Fig. 4. Depending on the specific control needs, customers can select an appropriate set of targeted temperature value from the pool of trade-off solution. Table 2 shows sample optimized solutions selected from derived pareto front. It can be seen that each approach is able to derive a high quality pareto front. Table 2. Sample trade-off solutions

Temperature (F) 68.000 72.008 73.751 77.999

f1(x) 0.9992 0.9992 0.9971 0.9869

f2(x) 38.7501 41.0166 42.0023 44.4045

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41

41.5

Energy Consumption

42

42.5

43

43.5

44

44.5

45

1

0.995

0.99

0.985

Comfort Level

Fig. 4. Pareto optimal front 85

80

Temperature (F)

75

70

65

60

55

50

Sensor Data With GA Without GA 2

4

6

8

10

12 14 Time (Hour)

16

18

20

22

24

Fig. 5. Set Point variation with and without GA

The comparison of set points prior and after the implementation of MOGA as applied in simulations to optimize set points depends on comfort function and power demand as defined in equation (1) and (2) as depicted in Fig. 5. Since, the variation in ambient environment parameter and observed set point variation within a day. In comparison to set points without MOGA, the set point and outside sensor data, error decreased after MOGA is applied. Fig. 6 describes the power consumption by the thermal controlling agent before and after MOGA is applied with regard to adjustment of set points.

Pervez Hameed Shaikh et al. / Procedia Technology 11 (2013) 99 – 106 48

46

44

Power Consumption (KW)

42

40

38

36

34

32 Without GA With GA 30

2

4

6

8

10

12 14 Time (Hour)

16

18

20

22

24

Fig. 6. Power Consumption with and without GA 1

0.99

Comfort

0.98

0.97

0.96

0.95 Without GA With GA 0.94

2

4

6

8

10

12 14 Time (Hour)

16

18

20

22

24

Fig. 7. Occupant’s comfort with and without GA

Fig. 7 shows the improved comfort level after MOGA has been applied. The inhabitants gain longer time when maximum comfort is maintained. Meanwhile, compared to average power consumption is considerably reduced in comparison to an increased comfort level. This MOGA is capable of balancing the total power consumption and customer comfort ultimately enhancing building automation intelligence.

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4. Conclusion In this study, a behavioural relation model has been developed along with control system design considering both energy efficiency and consumer comfort. Simulation results describe, that MOGA has significantly achieved control on energy consumption in comparison to customers' comfort level. In addition, the customer preference plays decisive role in achieving overall comfort. Thus, the designed control system with MOGA optimization is effective for energy and comfort management in implementing smart and energy efficient building envelopes. The optimization also verifies the rationality of the behavioural model relationship. Acknowledgements The authors are thankful to the Universiti Teknologi PETRONAS for technical and financial support to conduct research. References [1] FehmiGU, Ergun Y.A linear programming approach to household energy conservation: Efficient allocation of budget. Energy and Buildings, 49;2012. pp. 200–208. [2] LaustsenJ. Energy Efficiency Requirements in Building Codes, Energy Efficiency Policies for New Buildings, IEA Information Paper, International Energy Agency, Paris;2008. [3] Perez-Lombard L., Ortiz J., Pout C. 2008 “A review on buildings energy consumption information” Energy and Buildings 40 p. 394ˀ398. [4] Torcellini P, Pless S and Deru M.Zero energy buildings: A critical look at the definition.presented at the ACEEE Summer Study, Pacific Grove, CA;2006. pp.14−18. [5] Doukas, H, PatlitzianasKD.Intelligent building energy management system using rule sets. Building and Environment 10;2007.pp. 3562-3569. [6] PeetersL, de Dear R, HensenJ, D’haeseleer W.Thermal comfort in residential buildings: comfort values and scales for building energy simulation. Applied Energy 86, 2009. pp. 772-780. [7] Wong JKW, Li H, Wang SW. Intelligent building research: a review.Automation in Construction 14; 2005.pp. 143–59. [8] Al-Rabghi OM, Akyurt MM.A survey of energy efficient strategies for effective air conditioning. Energy Conversion and Management 45;2004.pp.1643–54. [9] Kua HW, Lee SE. Demonstration intelligent building—a methodology for the promotion of total sustainability in the built environment.Building and Environment 37;2002.pp. 231–40. [10] Mark A, Carpenter MS. BASE Quality Assurance Project Plan” Environmental Health and Engineering, 60 Wells Avenue Newton, MA 02159-3210 and Susan Womble, Office of Radiation and Indoor Air United States Environmental Protection Agency Washington, DC 20460; December 1996.pp. 24. [11] Yang R and Wang L. Multi-zone building energy management using intelligent control and optimization. Sustainable Cities and Society,6;2013.pp. 16-21. [12] Wang Z, Wang L, Dounis AI, and Yang R. Multi-agent control system with information fusion based comfort model for smart buildings.Applied Energy 99, 2012.p. 247-254. [13] Sanjib Mishra SKP. Short Term Load Forecasting using Neural Network trained with Genetic Algorithm & Particle Swarm Optimization.First International Conference on Emerging Trends in Engineering and Technology, IEEE DOI 10.1109/ICETET 94; 2008.pp. 2736. [14] Nayak SC, Misra BB, and Behera HS.Index prediction with neuro-genetic hybrid network: A comparative analysis of performance.in Computing, Communication and Applications (ICCCA),International Conference; 2012 . pp. 1-6.