In this paper, we develop a rigorous theoretical framework and a practical implementation for the evaluation of the energy- and cost-saving potential of buildings. The goal is to promote sustainability in the context of building energetics by achieving the most efficient, optimal exploitation of the financial resources available for refurbishments. The practical realization of this concept requires the evaluation of the optimal refurbishment cost that maximizes the net energetic or financial savings during the life cycle of the building. On the one hand, too small a refurbishment cost might lock-in a substantial amount of energy and cost savings potential. On the other hand, refurbishment costs that are too high due to unnecessarily implemented energy-saving measures are likely to waste financial resources. The key concept behind the theory is the novel definition of the reference value used for the computation of the energy- and cost-saving potentials. From a mathematical point of view, the reference value is obtained by two subsequent optimizations. First, a constrained, single-objective optimization is used to evaluate the best energetic state of the building as a function of the refurbishment cost. Second, a simple unconstrained search must be performed to obtain the minimum value and the minimum place of the one-dimensional cost function. The proposed framework automatically provides personalized solutions corresponding to the actual technical characteristics of the building. These solutions are optimal under the given circumstances of the actual refurbishment, resulting in either the highest possible energy- or cost-saving amounts during the life cycle of the building.

In this paper we apply bacterial memetic algorithms for the energy and cost optimal renovation of residential buildings. Following economical demands, there are two types of optimizations considered. First, the total cost of the renovation is prescribed and the best energy quality of the building envelope is determined with a total construction cost not exceeding the given limit. Second, the targeted energy quality of the renovated building is prescribed, and the algorithm determines the optimal renovation plan requiring the smallest costs. Only the optimization of the building envelope is performed, the optimization of the heating-ventilation-air-condition system is ignored. The chromosome of the bacteria contains genes taking only integer values and genes taking real values as well. The value of the genes taking only integer numbers are improved by a simple local search algorithm. The value of the genes taking real numbers are improved locally by the Levenberg-Marquardt approach. Results of actual building optimizations reveal the potential of the proposed algorithm. © 2012 IEEE.

The topic of energy efficient building design is an attractive philosophy in the era of diminishing fossil energy sources drained by the ever increasing thirst of the Earth's population for energy. In this paper we propose to apply evolutionary algorithms aiding the design of energy efficient buildings. We employ a simple mathematical model involving a large number of parameters. Energy efficiency is defined in terms of these parameters through algebraic evaluations. The quasi-optimal values of the fitness function representing the level of energy efficiency are obtained by Bacterial Evolutionary Algorithms. Results indicate that in the case of a prescribed total construction cost close to 80% improvement can be achieved in terms of energy efficiency. On the other hand, if one targets a prescribed energy efficiency, more than 30% of the total construction costs can be saved by proper optimization.