This paper developed a new method that adaptively adjusts a design space by considering the actual solution distribution of a problem to overcome the conventional design-space adaptation method that assumes the solutions distribution to be a normal distribution because the distributions of solutions are rarely normal distributions for real-world problems. The developed method was applied to nineteen multiobjective test functions that are widely used to evaluate the characteristics and performance of optimization approaches. The results showed that this method adapted the design space to an appropriate design space where the solution existence probability was high. The optimization performance achieved using the developed method was higher than that of the conventional methods. Furthermore, the developed method was applied to the conceptual design of an unmanned spacecraft to confirm its validity in real-world design and multidisciplinary-optimization problems. The results showed that the Pareto solutions of the developed method were superior to those of conventional methods. Additionally, the optimization efficiency with the developed method was improved by more than 1.4 times over that of the conventional methods. In this regard, the developed method has the potential to be applied to complicated real-world optimization problems to achieve better performance and efficiency.

In this article, a building electric energy prediction model using a Kriging method was developed for an efficient building energy management system (BEMS). In the prediction model, only easily obtainable weather factors such as temperature, humidity, wind speed, etc. were used as input parameters for actual application to the BEMS. In order to identify the effects of weather factors on building energy consumption, two data mining techniques were used: Analysis Of Variance (ANOVA) and Self-Organizing Map (SOM). The accuracy of the model using only easily obtain weather factors was compared with that of the model using the weather factors selected based on the results of data mining. According to the results, the building electric energy prediction model using only easily obtainable weather factors has sufficient predictive ability for BEMS. The developed building electric energy prediction model was applied to the optimization problem of charge/ discharge scheduling for an electric energy storage system. The results showed that the building electric energy prediction model has sufficient accuracy for application to the BEMS.