Universitas Bung Hatta

Menuju Perguruan Tinggi Berkelas Dunia

Bg Universitas Bung Hatta
Kamis, 25 Januari 2007 Keteknikan

Energy Analysis of an Air Conditioning System using Fuzzy Logic Controller

CONCLUSION

The controller i.e. FLC was developed to control the motor speed in order to maintain the room temperature at or close to the setpoint temperature. However, when the room is thermal loaded, the controller act such that the heat recovery to the room is fast until the temperature setpoint is upheld again. The energy consumption would change with the change in motor speed. When the motor speed increases, the room temperature decreases and the COP increases with the increase in energy consumption. Furthermore, the higher the energy consumption the smaller is energy saving.

The modification of FAM rules that produced satisfactory solution in term of energy saving for the room was employed. The impacts of FLC on the performance of the system, the room temperature and energy consumption have been analyzed experimentally. The study has shown that FLC is better than PID controller. The trend of compressor performance in term of COP is found to be similar for all temperature settings. Energy analysis shows that FLC gives the highest saving in comparison with on/off and PID controller. The main outcome of this study shows that using VSC and choosing suitable control strategy is capable to control the space temperatures with significant energy saving.

REFERENCES

[1] Nasution H. Energy analysis of an air conditioning system using PID and fuzzy logic controllers. Universiti Teknologi Malaysia: PhD Thesis; 2006.
[2] Nasution H and Wan Hassan MN. Saving energy for air conditioning with variable speed and proportional control system. Proc of the Malaysia Science and Technology Congress 2003, Kuala Lumpur, 2003.p.843-850.
[3] Nasution H and Wan Hassan MN. Variable speed motor of compressor for energy saving of air conditioning. Proc of the International Conference on Fluid and Thermal Energy Conversion 2003, Bali, 2003.p.053-1-053-9.
[4] Yu PCH. A study of energy use for ventilation and air-conditioning systems in Hong Kong. The Hong Kong Polytechnic University: PhD Thesis; 2001.
[5] Masjuki HH, Mahlia TMI and Choudhury IA. Potential electricity savings by implementing minimum energy efficiency standards for room air conditioners in Malaysia. Energy Conversion & Management 2001; 42: 439-450.
[6] Nasution H and Wan Hassan MN. Potential electricity savings by variable speed control of compressor for air conditioning systems. Clean Techn Environ Policy 2006; 8: 105-111.
[7] Nasution H and Wan Hassan MN. Energy saving for air conditioning by proportional control, variable and constant speed motor compressor. Proc of the 2nd International Conference on Mechatronics 2005. Kuala Lumpur, 2005.p.492-498.
[8] Nasution H. Variable speed drives of reciprocating compressor for air conditioning: Literature review. Jurnal SAINSTEK 2003; 6(1): 25-39.
[9] Huang S and Nelson RM. A PID-law-combining fuzzy logic controller for HVAC application. ASHRAE Trans 1991; 97: 768-774.
[10] Huang S and Nelson RM. Rule development and adjustment strategies of a fuzzy logic controller for an HVAC system: Part two – experiment. ASHRAE Trans 1994; 100: 851-856.
[11] Ling KV and Dexter AL. Expert control of air conditioning plant. Automatica 1994; 30(5): 761-773.
[12] Chen W, Zhu R and Wu Y. Membership functions optimization of fuzzy control based on genetic algorithms. Proc of the 1998 International Refrigeration Conference at Purdue, Indiana, 1998.p.207-211.
[13] Ho JK, Kim KS, Sim MS, Han KH and Ko BS. An application of fuzzy logic to control the refrigerant distribution for the multi type air conditioner. Proc of the 1999 IEEE International Fuzzy Systems Conference, Seoul, 1999.p.III-1350-III-1354.
[14] Yamada F, Yonezawa K, Sugawara S and Nishimura N. Development of air conditioning control algorithm for building energy saving. Proc of the 1999 IEEE International Conference on Control Applications, Hawai’i, 1999.p.1579-1584.
[15] Dounis AI and Manolakis DE. Design of a fuzzy system for living space thermal comfort regulation. Applied Energy 2001; 69: 119-144.
[16] Kolokotsa D, Tsiavos D, Stavrakakis GS, Kalaitzakis K and Antonidakis E. Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal visual comfort and indoor air quality satisfaction. Energy and Buildings 2001; 33: 531-543.
[17] Hamed B. Comparison of fuzzy logic and classical controller design for nonlinear system. New Mexico State University: PhD Thesis; 1999.
[18] Pasino KM and Yurkovich S. Fuzzy control. United State of America: Addison Wesley; 1998.
[19] Hussu A. Fuzzy control and defuzzification. Mechatronics 1995; 5(5): 513-526.
[20] Yasin SY. Systematic methods for the design of a class of fuzzy logic controllers. Western Michigan University: PhD Thesis; 2002.
[21] Chen Z. Consensus in group decision making under linguistic assessments. Kansas State University: PhD Thesis; 2005.
[22] Eker I and Torun Y. Fuzzy logic control to be conventional method. Energy Conversion & Management 2006; 47: 377-394.
[23] Bagis A. Determining fuzzy membership functions with tabu search-an application to control. Fuzzy Sets and Systems 2003; 139: 209-225.
[24] Huang S and Nelson RM. Rule development and adjustment strategies of a fuzzy logic controller for an HVAC system: Part one – analysis. ASHRAE Trans 1994; 100: 841-850.
[25] Bourke MM. Self learning predictive control using relational based fuzzy logic. University of Alberta: PhD Thesis; 1995.
[26] Palm R. Scaling of fuzzy controller using the cross-correlation. IEEE Trans on Fuzzy Systems 1995; 3: 116-123.
[27] Gaweda AE and Zurada JM. Data driven linguistic modeling using relation fuzzy rules. IEEE Trans on Fuzzy Systems 2003; 11: 121-134.
[28] Casillas J, Cordon O, Jessus MJD and Herrera F. Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Trans on Fuzzy Systems 2005; 13: 13-29.
[29] Joo M and Lee JS. A class of hierarchical fuzzy systems with constrains on the fuzzy rules. IEEE Trans on Fuzzy Systems 2005; 11: 194-203.

Dr. Ir. Henry Nasution, M.T
Staff Jurusan Teknik Mesin
Fakultas Teknologi Industri
Universitas Bung Hatta