This paper investigates the impact of change in household-type on residential electricity demand due to population aging in Japan considering the change in dwelling-type. For the purpose, existing forecasting method for number of household by dwelling-type is modified by segmentalizing the age class. A simulation demonstrates that the change in household-type during 2010-2030 would decrease the average residential electricity demand per household in 2030 by 0.9%. Moreover, considering the change in dwelling-type, the unit demand per household would decrease by 2.4% in Japan, which is distributed among 0.7% (Tokyo Metropolitan Area)~2.8% (Tohoku, Hokuriku). The simulation demonstrates that the impact of change in household-type is enhanced by considering the change in dwelling-type.

View full abstract

 The present study proposes an approach for obtaining a candidate value of a water hygiene standard using water quality monitoring datasets and acceptable infection risks of waterborne pathogenic microbes. In the proposed approach, posterior predictive distributions of the concentration of an indicator microorganism and a pathogen-derived gene are estimated separately based on monitoring datasets, and then the probabilistic distribution of concentration ratio between the indicator microorganism and the pathogen-derived gene is obtained. In addition to it, an acceptable concentration of a pathogen-derived gene in water is derived from an acceptable infection risk (10^-4 infections per person per year) by quantitative microbial risk assessment (QMRA). The candidate value of water hygiene standard is determined by collating the calculated acceptable concentration of a pathogen-derived gene and the estimated distribution of concentration ratio. When a safety factor value is set at 10, 150 most probable number/ 100mL of Escherichia coli was obtained as a candidate value of water hygiene standard for river water that is used as a drinking water source. Although this study identified research areas that have to be investigated further, such as the validation of QMRA assumptions, the proposed approach makes it possible to determine water hygiene standard values based on the scientific basis, including accumulated datasets of environmental water monitoring.

View full abstract

 This study improves thermal response test (TRT) by using a set of fiber-optic distributed temperature sensors (DTSs) to estimate effective thermal conductivities in multi-layered formations. These sensors are installed into both inlet and outlet pipes (a U-tube) to obtain vertical profiles of heat transfer fluid temperature at TRT in more detail than those obtained by single DTS as in previous studies. When the borehole is constructed in a formation with several horizontally layers, this study analyzes the effective thermal conductivity in each layer under the assumption of line heat source. In the analysis, the slopes of fluid temperature on logarithmic elapse time are averaged within each layer, and heat exchange rate is calculated using a sum of linear slopes of temperature with depth in both inlet and outlet pipes. Offset and averaging span for DTS calibration are evaluated by laboratory sink tests at constant temperatures. Finally, an example of the proposed method at Asahikawa City, Hokkaido, Japan, is shown. In the example, the formation consists of three layers; unconsolidated gravel, clay, and pre-Quaternary shale. The solutions of the effective thermal conductivity in each layer are converged commonly when the thicknesses for analysis are larger than 20 meters. The conductivity in the upper-gravelly layer is about 1.5 times larger than those in the mid-clayey and lower-shale layers, probably due to groundwater flow advection. The thickness-weighted average of conductivities in the three layers is almost equal to the conductivity by conventional TRT with Pt100 sensors. The solutions are also valid in terms of total heat exchange rate. In addition, a series of effective thermal conductivities is invariable among the cases of DTS data: 1) calibrated in the heating period, 2) smoothed additionally by the FFT low-pass filter, 3) randomly sampled as the boot-strap method, and 4) calibrated in the recovery period.

View full abstract