To secure a stable energy supply and bring renewable energy to buildings within a reasonable cost range, a hybrid energy system (HES) that integrates both fossil fuel energy systems (FFESs) and new and renewable energy systems (NRESs) needs to be designed and applied. This paper presents a methodology to optimize a HES consisting of three types of NRESs and six types of FFESs while simultaneously minimizing life cycle cost (LCC), maximizing penetration of renewable energy and minimizing annual greenhouse gas (GHG) emissions. An elitist non-dominated sorting genetic algorithm is utilized for multi-objective optimization. As an example, we have designed the optimal configuration and sizing for a HES in an elementary school. The evolution of Pareto-optimal solutions according to the variation in the economic, technical and environmental objective functions through generations is discussed. The pair wise trade-offs among the three objectives are also examined.

This paper presents an optimization method to design a solar water heating (SWH) system based on life cycle cost (LCC). A genetic algorithm is employed to optimize its configuration and sizing as the optimization technique. To ensure that the optimal solution obtained from the proposed method is a practical design, three constraint conditions, including the energy balance, solar fraction, and available space to install solar collectors, have been set. In addition, the real devices available in the marketplace are considered in the optimization process that searches for optimal configuration and sizing, which is represented by the type and number of each component. By using the proposed method, a SWH system in an office building, South Korea has been designed and optimized. It is observed that a low solar fraction does not always present a decrease in the LCC. A trade-off between the equipment cost and the energy cost results in an optimal design of the SWH system that yields the minimum LCC.

There are several methods to obtain the in situ thermal transmittance value (U-value) of building envelopes from on-site data, including the three approaches of the progressive average method, average method considering the thermal storage effect, and dynamic method for deriving the U-value from heat flowmeter (HFM) measurements and the four methods with different formulas to analyze infrared thermography (IRT) measurement data. Since each of these methods considers different parameters and the non-steady characteristics of the heat transfer in building walls in their own way, discrepancies may occur among the obtained results. This study evaluates and compares the in situ U-values by using various methods of analyzing HFM and IRT measurement data. Further, by investigating buildings with similar materials and identical stratigraphies, but with different construction years, we analyze the discrepancy between the designed and measured values caused by material deterioration and evaluate the errors according to the analysis method. The percentage deviation between the U-values obtained by the three methods from the HFM data is found to be satisfactory, being within 10%. When compared with the results of the progressive average method, the deviations for the four different IRT-measurement-based methods vary greatly, being in the range of 6–43%.

In the last few decades, an average method which is regulated by ISO 9869-1 has been used to evaluate the in situ thermal transmittance (U-value) and thermal resistance (R-value) of building envelopes obtained from onsite measurements and to verify the validity of newly proposed methods. Nevertheless, only a few studies have investigated the test duration required to obtain reliable results using this method and the convergence characteristics of the results. This study aims to evaluate the convergence characteristics of the in situ values analyzed using the average method. The criteria for determining convergence (i.e., end of the test) using the average method are very strict, mainly because of the third condition, which compares the deviation of two values derived from the first and last periods of the same duration. To shorten the test duration, environmental variables should be kept constant throughout the test or an appropriate period should be selected. The convergence of the in situ U-value and R-value is affected more by the length of the test duration than by the temperature difference if the test environment meets literature-recommended conditions. Furthermore, there is no difference between the use of the U-value and R-value in determining the end of the test.

To maximize the energy performance and economic benefits of solar water heating (SWH) systems, the installation and operation-related design variables as well as those related to capacity must be optimized. This paper presents a novel design method for simultaneously optimizing the various design variables of an indirect forced-circulation SWH system that is based on the life cycle cost and uses a genetic algorithm. The effectiveness of the proposed method is assessed by evaluating the long-term performance corresponding to four cases, which are optimized using different annual solar fractions and sets of the design variables. When the installation and operation-related design variables were taken into consideration, it resulted in an efficient and economic design and an extra cost reduction of 3.2%–6.1% over when only the capacity-related design variables were considered. In addition, the results of parametric studies show that the slope and mass flow rate of the collector have a significant impact on the energy and economic performances of SWH systems. In contrast, the mass flow rate in the secondary circuit and the differences in the temperatures of the upper and lower dead bands of the differential controller have a smaller impact.

In this study, the multi-objective optimization of an indirect forced-circulation solar water heating (SWH) system was performed to obtain the optimal configuration that minimized the life cycle cost (LCC) and maximized the life cycle net energy saving (LCES). An elitist non-dominated sorting genetic algorithm (NSGA-II) was employed to obtain the Pareto optimal solutions of the multi-objective optimization. To incorporate the characteristics of practical SWH systems, operation-related decision variables as well as capacity-related decision variables were included. The proposed method was used to conduct a case study wherein the optimal configuration of the SWH system of an office building was determined. The case study results showed that the energy cost decreases linearly and the equipment cost increases more significantly as the LCES increases. However, the results also showed that it is difficult to identify the best solution among the Pareto optimal solutions using only the correlation between the corresponding objective function values. Furthermore, regression analysis showed that the energy and economic performances of the Pareto optimal solutions are significantly influenced by the ratio of the storage tank volume to the collector area (RVA). Therefore, it is necessary to simultaneously consider the trade-off and the effect of the RVA on the Pareto optimal solutions while selecting the best solution from among the optimal solutions.