We have developed an energy-saving operations system featuring remote operation of central monitoring equipment installed in a building. This system applies robotic process automation to remote operation to automatically perform energy-saving operations on behalf of the operations manager. Furthermore, as another feature, the system requires only a local area network to connect to the central monitoring equipment enabling automatic operation to be performed regardless of the specifications of the central monitoring equipment. The items targeted for energy-saving operation by this system are the optimal operation of a heat source system, setting of the supply water temperature of heat source equipment, setting of room temperature, and setting of outside-air intake volume. At present, the operations manager has the role of performing these energy-saving operations, but finding the optimal value for each of these operations is a difficult task. An operations manager, moreover, is responsible for tasks other than facility operations such as maintenance management, so changing optimal settings accurately at regular intervals on an ongoing basis can be quite a burden. This system uses robotic process automation technology, so it is capable of performing all energy-saving operations that can be executed by the central monitoring equipment. We installed this system in a large-scale shopping mall and performed energy-saving operations on outside-air processing units. In this trial, we achieved a 44% reduction in the amount of energy required for outside-air processing and a 47% reduction in CO₂ emissions.

The Coefficient of Performance of an ordinary water-cooled chiller is presented as a relationship with the chiller load factor and cooling water temperature. However, the cooling water temperature fluctuates according to the processed heat of the cooling tower originating in the cooling energy of the chiller and to the outside temperature and humidity. It is therefore difficult to obtain the cooling water temperature under the processed-heat and weather conditions at the time of evaluation. This, in turn, makes it difficult to determine the Coefficient of Performance of a water-cooled chiller at the evaluation time. In this research, we formulated the Coefficient of Performance of a water-cooled chiller as a relationship with the chiller load factor and specific enthalpy of outside air. Specifically, we used the Number of Transfer Units (NTU) model of a cooling tower to calculate the cooling water temperature corresponding to the cooling-tower load factor targeting a counterflow cooling tower for a range of values of outside-air specific enthalpy. This technique makes it possible to evaluate the Coefficient of Performance of a water-cooled chiller without determining the cooling water temperature. Furthermore, for the case of installing multiple units of chillers, it becomes possible to calculate the composite Coefficient of Performance of those chillers without having to determine the cooling water temperatures for the different operation load factors of those chillers. Moreover, since the composite Coefficient of Performance can be calculated by combining the different installation capacities of these chillers, the energy consumption of multiple chillers can be calculated at the basic planning stage.

Advances in next‐generation sequencing (NGS) technologies since 2005 have revolutionized biological science. One particular application of NGS technologies is to elucidate microbiomes in built environments. We are currently conducting a series of studies on the elucidation and control of mass infection mechanisms based on dynamic measurement of environment microbiomes. The objective of this study is to clarify the dispersion characteristics of oral bacteria in the built environment. Bacterial communities from occupants’ hands and oral cavities, doorknobs, desk and keyboard surfaces, and air in laboratories were investigated in seven Japanese universities. The median relative abundances of Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, and Fusobacteria were 41%, 31%, 12%, 7%, and 3%, respectively. Moreover, the main genera detected were Streptococcus (27.6%), Haemophilus (7.0%), Staphylococcus (5.6%), Neisseria (5.6%), Corynebacterium (4.7%), Rothia (3.2%), Prevotella (3.0%), Fusobacterium (2.6%), Veillonella (1.7%), Leptotrichia (1.7%), Enhydrobacter (1.7%), Lactobacillus (1.3%), Acinetobacter (1.3%), and Actinomyces (1.1%). The oral bacteria Actinomyces, Corynebacterium, Fusobacterium, Haemophilus, Leptotrichia, Neisseria, Prevotella, Rothia, and Streptococcus were observed in indoor air and on surfaces as well as in oral cavities. Furthermore, Prevotella melaninogenica and Rothia mucilaginosa were observed in all samples, including those from hands and oral cavities, doorknobs, desk and PC keyboard surfaces, and air in laboratories, in all seven universities.

In data centers, heating, ventilation, and air-conditioning (HVAC) consumes 30–40% of total energy consumption. Of that portion, 26% is attributed to fan power, the ventilation efficiency of which should thus be improved. As an alternative method for experimentations, computational fluid dynamics (CFD) is used. In this study, “parameter tuning”—which aims to improve the prediction accuracy of CFD simulation—is implemented by using the method known as “design of experiments”. Moreover, it is attempted to improve the thermal environment by using a CFD model after parameter tuning. As a result of the parameter tuning, the difference between the result of experimental-measurement results and simulation results for average inlet temperature of information-technology equipment (ITE) installed in the ventilation room of a test data center was within 0.2 °C at maximum. After tuning, the CFD model was used to verify the effect of advanced insulation such as raised-floor fixed panels and show the possibility of reducing fan power by 26% while keeping the recirculation ratio constant. Improving heat-insulation performance is a different approach from the conventional approach (namely, segregating cold/hot airflow) to improving ventilation efficiency, and it is a possible solution to deal with excessive heat generated in data centers.

Hot water supply is one of the leading consumers of energy in the building sector in cold climate areas. The use of woody biomass is effective in reducing CO₂ emissions in hot-water supply systems. This report deals with a system that combines a wood pellet boiler (PB) and a heat pump system with CO₂ (CO₂HP) that is used in a facility for disabled people. The following research was conducted. The operation of a hybrid system combining a PB and CO₂HPs was investigated. While operating the system, four specific operations were developed as countermeasures to save on costs and reduce system troubles while reducing CO₂ emissions. The processes and results are introduced. Numerical simulations were carried out to optimize the operation. The hot water temperature, water volume, and hot water loads were simulated. The influence of the water volume ratio on the cost and primary energy consumption under the requirements for safe system operation was studied. The regional economic ripple effects (REREs) of this system were studied. The wood pellet boiler is not only a measure for reducing primary energy consumption but can also play an important role in a regional economy for sustainable development in countries that import energy resources such as Japan.