Optimization Implementation and Sustainable Measures

As the consideration of how the building sector impacts the environment increases, the evolution of green measures and assessment tools has risen accordingly. Great attention has been drawn towards improving the sustainability of the buildings to mitigate the earlier mentioned impacts.

Consequently, there are numerous of alternatives that can be applied in each sustainability aspect to improve its performance and in turn improve the overall building performance as well. However, there may be some alternatives that perform better than the others, and using trial and error is exhaustive and may lead to unreliable solutions. Hence, the optimization concept has been evolved, which has a great advantage in finding the optimal or near optimal alternatives (Wang et al., 2005).

The following discussion will illustrate some of the research works that applied optimization techniques in the upgrading building performance of a single or multi-aspects.

Wang et al (2005) proposed a multi-criterion optimization algorithm utilizing the multi-objective genetic algorithm. This research aimed to provide the designers with a set of alternatives which could upgrade the building envelope performance within minimal LCC. The performance of the building envelope was determined by implementing life cycle environmental impact. The advantage of this research is utilizing the concept of LCC to determine the optimal or the near optimal upgrade alternative, but it assesses only one aspect which is the building envelope performance.

Also, Magnier and Haghighat (2010) introduced a multi-objective optimization by applying Non- Dominated Sorting Genetic Algorithm (NSGA-II). The research had two objectives 1) maximizing the thermal comfort of the building, which is translated to the Predicted Mean Vote (PMV) that was representative of what a large population would think of a thermal environment; and 2) minimizing the energy consumption of heating, cooling, and fan energy. The decision variables were related to HVAC components and building envelope elements. The limitation of this study was not integrating the economic objectives in the optimization process by considering the upgrade cost in the optimization process, because most the proposed alternatives may be inefficient in economic terms.

Bichioua and Krarti (2011) proposed a single objective optimization to minimize the LCC of the introduced HVAC and Building Envelope alternatives to achieve the required thermal comfort for a two-story residential building. This research simulated the building into five cities applying DOE-2 energy simulation software. Each of the five scenarios were introduced to three different optimization algorithms which are Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Sequential Search optimization (SS). The results revealed that evolutionary algorithms such as GA and PSO consumed less computational time than SS. The benefit of this research is utilizing the LCC approach to evaluate the proposed upgrade alternatives; however, it only considers the HVAC and building envelope decision variables.

Furthermore, Marzouk et al (2011) performed a multi-objective optimization to maximize the achieved LEED credits for new construction when using green materials, while minimizing the resulted total cost. The research applied multi-objective ant colony optimization (ACO) technique that was implemented on a residential building of two stories. The output shows the near optimal

solutions that was represented through the Pareto frontier. However, this study considered only one green aspect – namely, materials – also, it considered only the total cost and overlooked the life cycle cost which may change the solution when consider time value of money.

Simmons et al (2013) introduced an optimization combinatoric model rather than the evolutionary algorithms. This idea arose from the fact the properties of the building technologies have a discrete nature and their selection nature is a combinatoric problem. The research claimed that there are 170 million unique combinations. The main objective was to explore this combinatorial space of technology alternatives and to find low cost solutions that meet the energy saving goals. Besides, this optimization algorithm aimed to minimize the sum of the premium monetary cost, which is the cost of any technology’s level of achievement cost minus baseline cost. However, this study overlooked the time value of money which represented in LCC because this research aimed to meet the instantaneous energy reduction at the time of construction at minimum capital cost.

Another limitation is the increased computational time load required for searching all the available combinations in which this computational loading could be decreased when applying the optimization evolutionary algorithms.

Additionally, Abdallah et al (2015; 2014) developed an optimization model that could minimize the upgrade cost of selected green measures that can achieve certain level required by LEED for existing building. The optimization model applied genetic algorithm by utilizing a single objective, which was minimizing the upgrade cost for the alternatives that are proposed to upgrade different LEED categories to a specified level. There are some limitations of the developed model which are utilizing upgrade cost rather than the LCC cost which may change the final output. Also, using

a single objective optimization leads to a unique solution, while multi-objective optimization can provide the decision makers with set of different trade-offs.

In another research developed by Juan et al (2010), a hybrid decision support system was implemented using genetic algorithm and A* search techniques to get the optimal upgrade alternatives for a building sustainability with minimal upgrade cost. The research demonstrated the shortcoming of each of the utilized techniques when implemented separately, however the results of the research showed the robustness of combining the two techniques to solve large-scale zero-one programming determinate problem effectively. Despite the advantages of the proposed hybrid system, the system provided only one optimal solution in each run as it utilized a single objective optimization function that minimized the upgrade cost. Additionally, it overlooked the LCC concept in considering the renovation alternatives.

Generally, the multi objective optimization is proved to be superior than the single objective one (Wang et al., 2005). When two objectives are treated separately or combined into one meta objective the optimal solution that is obtained in a single run is only one optimal solution. Hence, the decision maker cannot learn about the impact of the change of one criterion on the other.

Therefore, it is difficult to make cost effective decisions without knowing the possible tradeoffs.

This dilemma is overcome in a multi-objective optimization method in which the multi- objectives are solved simultaneously resulting in a set of trade-offs.