Multi-criteria decision support system of the photovoltaic and solar thermal energy systems using the multi-objective optimization algorithm.

When the photovoltaic (PV) and solar thermal energy (STE) systems, which share the rooftop area, are installed in the same building, a trade-off problem occurs in terms of the energy, economic, and environmental aspects, and thus, steps need to solve this problem. Therefore, this study aimed to develop a multi-criteria decision support system of the PV and STE systems using the multi-objective optimization algorithm. This system was developed in the following six steps: (i) database establishment; (ii) designing the variables of the PV and STE systems; (iii) development of the analysis engine of the PV and STE systems; (iv) environmental and economic assessment from the life cycle perspective; (v) integrated multi-objective optimization (iMOO) with a genetic algorithm; and (vi) establishment of a multi-criteria decision support system.

Framework for the mapping of the monthly average daily solar radiation using an advanced case-based reasoning and a geostatistical technique.

For the effective photovoltaic (PV) system, it is necessary to accurately determine the monthly average daily solar radiation (MADSR) and to develop an accurate MADSR map, which can simplify the decision-making process for selecting the suitable location of the PV system installation. Therefore, this study aimed to develop a framework for the mapping of the MADSR using an advanced case-based reasoning (CBR) and a geostatistical technique. The proposed framework consists of the following procedures: (i) the geographic scope for the mapping of the MADSR is set, and the measured MADSR and meteorological data in the geographic scope are collected; (ii) using the collected data, the advanced CBR model is developed; (iii) using the advanced CBR model, the MADSR at unmeasured locations is estimated; and (iv) by applying the measured and estimated MADSR data to the geographic information system, the MADSR map is developed.

Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning.

The photovoltaic (PV) system is considered an unlimited source of clean energy, whose amount of electricity generation changes according to the monthly average daily solar radiation (MADSR). It is revealed that the MADSR distribution in South Korea has very diverse patterns due to the country's climatic and geographical characteristics. This study aimed to develop a MADSR estimation model for the location without the measured MADSR data, using an advanced case based reasoning (CBR) model, which is a hybrid methodology combining CBR with artificial neural network, multiregression analysis, and genetic algorithm.

A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption.

The number of deteriorated multi-family housing complexes in South Korea continues to rise, and consequently their electricity consumption is also increasing. This needs to be addressed as part of the nation's efforts to reduce energy consumption. The objective of this research was to develop a decision support model for determining the need to improve multi-family housing complexes.

Economic and environmental evaluation model for selecting the optimum design of green roof systems in elementary schools.

Green-roof systems offer various benefits to man and nature, such as establishing ecological environments, improving landscape and air quality, and offering pleasant living environments. This study aimed to develop an optimal-scenario selection model that considers both the economic and the environmental effect in applying GRSs to educational facilities. The following process was carried out: (i) 15 GRSs scenarios were established by combining three soil and five plant types and (ii) the results of the life cycle CO(2) analyses with the GRSs scenarios were converted to an economic value using certified emission reductions (CERs) carbon credits.