Optimized Power Control Methodology Using Genetic Algorithm

Providing an energy efficient environment to the occupants of the residential buildings is an interesting area of research. In the literature a number of techniques have been proposed for energy management, but the trade-off between users comfort index and energy consumption is still a challenge and unsolved. Previously we have proposed PSO based power control methodology. Our technique achieved good performance up-to some extent. In this paper, we propose an improved optimized power control methodology for occupants comfort index, energy saving and energy prediction using genetic algorithm (GA). Our proposed GA based optimized technique improved the occupants comfort index and consumed minimum power as compare to our previous work. Here our focus is to increase occupants comfort index, minimize energy consumption and comparison of power consumption using GA and PSO based predicted systems. GA based predicted system consumed less power as compare to its counterpart PSO based predicted system. The output and comparative results show the efficiency of the proposed method in increasing the occupant’s comfort index and minimizing energy consumption.

[1]  Kamel Ghali,et al.  Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm , 2009 .

[2]  Ryozo Ooka,et al.  Application Multi-Objective Genetic Algorithm for Optimal Design Method of Distributed Energy System , 2009 .

[3]  S. Emmerich,et al.  State-Of-The-Art Review of Co2 Demand Controlled Ventilation Technology and Application , 2003 .

[4]  S. F. Ghaderi,et al.  Integration of Artificial Neural Networks and Genetic Algorithm to Predict Electrical Energy consumption , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[5]  Kostas Kalaitzakis,et al.  Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks , 2002 .

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  Recep Yumurtaci Role of energy management in hybrid renewable energy systems: case study-based analysis considering varying seasonal conditions , 2013 .

[8]  Do-Hyeun Kim,et al.  Effective and Comfortable Power Control Model Using Kalman Filter for Building Energy Management , 2013, Wirel. Pers. Commun..

[9]  Ali Azadeh,et al.  INTEGRATION OF GENETIC ALGORITHM, COMPUTER SIMULATION AND DESIGN OF EXPERIMENTS FOR FORECASTING ELECTRICAL ENERGY CONSUMPTION , 2007 .

[10]  Ming-hai Li,et al.  Optimization for the Chilled Water System of HVAC Systems in an Intelligent Building , 2010, 2010 International Conference on Computational and Information Sciences.

[11]  C. Bénard,et al.  Optimal Building Energy Management: Part II—Control , 1992 .

[12]  Philomena M. Bluyssen,et al.  Comfort of workers in office buildings: The European HOPE project , 2010 .

[13]  Lingfeng Wang,et al.  Multi-agent intelligent controller design for smart and sustainable buildings , 2010, 2010 IEEE International Systems Conference.

[14]  Jonathan A. Wright,et al.  Optimization of building thermal design and control by multi-criterion genetic algorithm , 2002 .

[15]  Lingfeng Wang,et al.  Multi-agent control system with information fusion based comfort model for smart buildings , 2012 .

[16]  Hongwei Li,et al.  Thermal-economic optimization of a distributed multi-generation energy system¿A case study of Beijing , 2006 .

[17]  Tina Yu,et al.  Modeling Occupancy Behavior for Energy Efficiency and Occupants Comfort Management in Intelligent Buildings , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[18]  Lingfeng Wang,et al.  Multi-agent control system with intelligent optimization for smart and energy-efficient buildings , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[19]  J. F. Kreider,et al.  Neural networks applied to buildings -- A tutorial and case studies in prediction and adaptive control , 1996 .

[20]  Kazuhiko Kudo,et al.  Multiple-purpose Operational Planning of Fuel Cell and Heat Pump Compound System using Genetic Algorithm , 2003 .

[21]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[22]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[23]  H. N. Lam,et al.  Using genetic algorithms to optimize controller parameters for HVAC systems , 1997 .

[24]  Geoff Levermore,et al.  Building Energy Management Systems: An Application to Heating, Natural Ventilation, Lighting and Occupant Satisfaction , 2000 .

[25]  Concettina Marino,et al.  Proposal of comfort classification indexes suitable for both single environments and whole buildings , 2012 .

[26]  Andrew Kusiak,et al.  A data-driven approach for steam load prediction in buildings , 2010 .

[27]  Lotfi A. Zadeh,et al.  Fuzzy Algorithms , 1968, Inf. Control..