The paper investigates overview of construction process of a 1 MW class floating photovoltaic (PV) generation structural system fabricated with fiber reinforced polymer (FRP) members. The floating PV generation system consists of unit structures linked by a hinge type connection of which the effect of bending moment between the unit structures, induced by the unstable movement of the water surface, was minimized. Moreover, the unit structures were classified into three types of structures by combining the floating PV generation system and pontoon bridges, which are constructed to install the electrical equipment and a route of movement for workers. The structural safety of the connection system among the unit structures and/or the mooring system is confirmed by referring to the relevant design codes. In addition, structural analysis using the finite element method was performed to ensure the safety of the floating PV generation structure, and commercial viability evaluation was performed based on the construction cost. The FRP member shows superior performance in construction and cost effectiveness in a floating PV generation system.

Steel fiber embedded in concrete serves to reduce crack development and prevent crack growth at the macroscopic level of the concrete matrix. Steel fiber-reinforced concrete (SFRC) with high compressive concrete strength is affected primarily by the dimensions, shape, content, aspect ratio, and tensile strength of the embedded steel fiber. In this study, double-ended hook steel fiber was used in SFRC with a concrete compressive strength of 80 MPa. This fiber was used for the study variables with two aspect ratios (64, 80) and tensile strength values up to 1600 MPa. The flexural performance of the SFRC specimens was evaluated using crack mouth open displacement tests, and the test results were compared with code provisions. A modified reinforcement index was also used to quantify the flexural performance based on comparisons with fracture energy.

This study was conducted to develop an energy consumption model of a chiller in a heating, ventilation, and air conditioning system using a machine learning algorithm based on artificial neural networks. The proposed chiller energy consumption model was evaluated for accuracy in terms of input layers that include the number of input variables, amount (proportion) of training data, and number of neurons. A standardized reference building was also modeled to generate operational data for the chiller system during extended cooling periods (warm weather months). The prediction accuracy of the chiller’s energy consumption was improved by increasing the number of input variables and adjusting the proportion of training data. By contrast, the effect of the number of neurons on the prediction accuracy was insignificant. The developed chiller model was able to predict energy consumption with 99.07% accuracy based on eight input variables, 60% training data, and 12 neurons.

This study focuses on investigating the effects of particle size and cross-linking density on the hygral behavior of superabsorbent polymers (SAPs), which are increasingly used as an internal curing material for high-performance concrete. Four SAPs with different mean particle diameters and cross-linking densities were tested under controlled wetting and drying conditions to measure free absorption and desorption kinetics. Absorption capacities of SAPs under actual mixing conditions were additionally measured and verified by means of mortar flow and semi-adiabatic hydration heat measurements. In addition, the effects of SAP type and dosage (i.e., 0.2, 0.4, and 0.6% by mass of cement) on the mechanical properties of hardened mortar were assessed. The results indicated that: (1) the absorption capacity increased with decreased cross-linking density and increased particle size under both load-free and mixing conditions; and (2) the greater the cross-linking density and the lower the particle size, the shorter the desorption time. It was also confirmed that while the early-age mechanical properties were more related with the gel strength of swollen SAP, the later-age mechanical properties were more affected by the water retention capacity and spatial distribution of SAP in the matrix.

Air conditioning in buildings accounts for 60% of the total energy consumption. Therefore, accurate predictions of energy consumption are needed to properly manage the energy consumption of buildings. For this purpose, many studies have been conducted recently on the prediction of energy consumption of buildings using machine learning techniques. The energy consumption of the air handling unit (AHU) and absorption chiller in an actual building’s air conditioning system is predicted in this paper using prediction models that are based on artificial neural networks (ANNs), which simply and accurately allow us to forecast energy consumption with limited variables. Using these ANN models, the energy usage of the AHU and chiller could be predicted by collecting a month’s worth of driving data during the summer cooling period. After the forecast models had been verified, the AHU prediction model showed performance in the ranges of 13.27% to 15.25% and 19.42% to 19.53% for the training period and testing period, respectively, and the mean bias error (MBE) ranges were 4.03% to 4.97% and 3.48% to 4.39% for the training period and testing period, respectively. The chiller prediction model satisfied the energy consumption forecast performance criteria presented by American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) guideline 14 (the measurement of energy and demand savings), with a performance of 24.64~25.58% and 7.12~29.39% in the training period and testing period, respectively, and MBE ranges of 2.59~3.40% and 1.35~2.87% in the training period and testing period, respectively. When the training period and testing period were combined for the AHU data, the actual energy usage forecast showed a lower error rate range of 0.22% to 1.11% for the training period and 0.17% to 2.44% for the testing period. For the chiller data, the error rate range was 0.22% to 2.12% for the entire training period, but was somewhat higher at 11.67% to 15.18% for the testing period. The study found that, even if the performance criteria were met, high accuracy results were not obtained, which was due to the poor data set quality. Although the forecast model based on artificial neural network can achieve relatively high-accuracy results with sufficient amounts of data, it is believed that this will require a thorough verification of the data used, as well as improvements in the predictive model to avoid overfitting and underfitting, to achieve such good results.

In order to mitigate air pollution problems caused mainly by the excessive emission of carbon dioxide, in 2012, the South Korean government decided to introduce a renewable portfolio standards (RPS) program that requires electricity providers to gradually increase their production of renewable energy. In order to meet the government’s target through this RPS program, electricity providers in Korea have looked to various types of new and renewable energy resources, such as biomass, wind, and solar. Recently, floating photovoltaic (PV) systems have attracted increased interest in Korea as a desirable renewable energy alternative. This paper provides a discussion of recent research into floating PV systems and the installation of floating PV power plants in Korea from 2009 to 2014. To date, thirteen floating PV power plants have been installed in Korea, and several plans are underway by many different organizations, including government-funded companies, to install more floating PV power plants with various generation capacities. These building trends are expected to continue due to the Korean government’s RPS program.