**Introduction**

The energy performance of a building depends on a high number of parameters. It is determined by its response as a complete system to the outdoor environment and the indoor conditions. Improved levels of performance require the coherent application of measures which altogether optimize the performance of the complete building system. Given the number of individual attributes that have to be combined to make a single building, the number of possible designs is very large, and determining the most efficient one is a complex problem.

Optimization of building energy performance is more complex in the case of Indian buildings. While in some cold European regions only heating energy consumption is usually considered, the Indian climate makes it essential to consider both heating and cooling energy uses. Varying some parameters of the building over their ranges of practical values can have opposite effects on heating and cooling energy consumptions. It is evident that an insulated building envelope helps in reducing the heating demand. But in summer, the outdoor night temperature being generally lower than the required indoor temperature, un-insulated but high thermal capacity walls allow for the evacuation of the heat stored in the building during the day, leading to the reduction of air-conditioning need. One important question is raised: what is the wall composition that leads to the lowest energy consumption in both seasons? The answer is not straightforward.

The main characteristics of the two sided problem are: a large multi-dimensional space to be searched, a range of different variable types and a non-linear objective function. Using genetic algorithms to solve such problems is a good alternative that allows us to identify not only the best design, but a set of good solutions.

**Design variables**

In cold countries there is not a real need for summer air-conditioning except where internal gains are high such as concert halls or opera houses. Our situation being different, in the present work, the objective function can be taken as being the sum of the heating and air conditioning energy loads.

In order to find the optimal design of a building, we have to compare the energy performance of a large number of configurations, which needs the computation of the heating and cooling loads for each of them. In the optimization approach, we propose to use a simplified procedure that is more straightforward and easier.

The losses across the envelope and the gross free gains depend on the lateral surface of the building, the type of used partitions as well as glazed surfaces on each of the facades. The shape and the dimensions of the solar protections have direct impact on the amount of the solar free gains received by the glazed areas. The vastness of the optimization problem would itself be a problem; therefore we have defined a set of possible configurations, by combining different cases of these design variables, taken inside reasonable values. The resulting set of configurations defines the space of research of our problem.

While keeping a constant volume, we can vary the dimensions of the building envelope and its shape. We can consider a simple cell-test having a rectangular shape with a fixed volume V or similarly a fixed floor area. For the opaque partitions i.e. walls and roofs we can consider different types of roofing (based on their insulation) and different kinds of walls (with different inertia and levels of insulation). Facades of the building can also be glazed, for such a case we can choose between simple and double glazing that differ by their transmission.

An efficient solar protection should allow for minimizing the cooling load without excessive increase in the heating load. This means that the shadowed portion of the glazed area should be as large as possible in summer and as low as possible in winter. Knowledge of the shaded part is necessary to compute the gross solar gains. The efficiencies of different sun shading devices can be adjudged from there “solar factors”; they are defined as the ratios of the received solar radiation in the presence of the shadowing device over the radiation that would be received in its absence.

Courtyards are considered ‘the spaces through which a building breathes’. They are an efficient element of passive feature in a building. However there is an optimal size for a courtyard; a very large courtyard breaks the unity of the building while a small one becomes more like a duct. A building with a given foot-print needs a courtyard that is a fixed percentage of the foot-print area. This criterion may form one of constraints in our case.

**Genetic algorithms**

Genetic algorithms have proved their efficiency in dealing with different optimization problems such as the optimization of building thermal design and control and solar hot water systems as well as the design of thermally comfortable buildings and the control of artificial lights. These techniques belong to a class of probabilistic search methods that strike a remarkable balance between exploration and exploitation of the search space. Genetic algorithms are initiated by selecting a population of randomly generated solutions for the considered problem. They move from one generation of solutions to another by evolving new solutions using the objective evaluation, selection, crossover and mutation operators.

A basic genetic algorithm has three main operators that are carried out at every iteration:

- Reproduction: chromosomes or solutions of the current generation are copied to the next one with some probability based on the value they achieve for the objective function which is also called fitness.
- Crossover: randomly selected pairs of chromosomes are mated creating new ones that will be inserted in the next generation.
- Mutation: it is an occasional random alteration of the allele of a gene.

While the selection operator for reproduction is useful for creating a new generation that is globally better than the preceding one, crossover brings diversity to the population by handling the genes of the created chromosomes and mutation introduces the necessary hazard to an efficient exploration of the research space. It makes the algorithm likely to reach all the points of research space. Before developing a genetic algorithm, we must choose the encoding that will be used to represent an eventual solution of the problem by a chromosome where the value of each variable is represented by one or several genes. The quality of the developed algorithm depends essentially on the adopted encoding strategy and its adequacy to the used crossover and mutation operators, while respecting the nature of variables and the constraints of the problem.

**The developed algorithm**

In this work, a genetic algorithm needs to be developed in order to provide a method for obtaining a set of optimal architectural configurations. There are few things which are quite clear even before we start, for example, having a large southern facade is beneficial because it is the sunniest in winter and the least in summer. But it is not desirable to have a building with a large lateral surface because it increases the heat loss through the envelope. A compromise needs to be worked out in such type of area.

**Conclusion**

The energy problem presented in this paper is particularly interesting. While it is relatively easy to find the best characteristics of a building under winter or summer conditions separately, tackling the two problems simultaneously is more complex. There is a trade-off that has to be done between the two seasons requirements. An optimization algorithm coupling the genetic algorithms’ techniques to the thermal assessment tool needs to be developed for Indian buildings. This algorithm further can be used to identify the best configurations from both energetic and economic points of view. Genetic algorithms represent a simple and very efficient approach for the solution of non-linear combinatorial optimization problems. Although Genetic Algorithms find good solutions without exploring the whole space of research, yet they need the evaluation of a large number of building configurations. The algorithm presents also the big advantage of converging not only toward the best solution but toward a set of configurations all of a high quality and diverse enough to allow the user to choose the most adequate one to his personal considerations that are not necessarily quantifiable. The fact that the required result is a set of very good solutions (and not the best one) means that good evaluation accuracy is sufficient.

Successful radiant heating or cooling is measured by the mean radiant temperature (“MRT”) of the space, and the energy used to keep people comfortable. Mean radiant temperature is simply a weighted average of the temperatures of all surfaces in a room (including people and equipment), with each temperature weighted by the size of the area at that temperature.

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