In Which Time Slots Can People Save Power? An Analysis Using a Japanese Survey on Time Use

Abstract

Residential demand-side management (DSM) of electricity has been gaining attention as a way to reduce energy consumption at home and as a way of maximizing the utilization of fluctuating solar power generation. To promote the smooth introduction of DSM into homes, power usage trends according to the time of the day should be examined for individuals in relation to their lifestyles. The analyses of power usage trends can identify the types of home appliances that should be utilized differently in order to increase energy efficiency. Such analyses can also predict the individual behavioral changes that should result in home appliances being used in the time slots in which solar power is more conveniently available. The purpose of this research was to estimate and observe the amount of power saving potential for each daily time slot with respect to an individual’s particular attributes, and to derive the power saving potential of the whole country by accumulating these data on individuals. This was achieved by using the Survey on Time Use and Leisure Activities (STULA) and Energy-Saving Performance Catalog (ESPC) in Japan. According to the results of our estimation, a meaningful power saving potential is sufficient to address a power supply shortage after a disaster such as an earthquake. It is possible to save power by replacing existing home appliances with more energy efficient ones, by making environmentally conscious choices when using home appliances, and by discontinuing the use of home appliances during electricity shortages within the community as a whole. Using the estimated power saving potentials, we examined the effects of two DSMs: (1) adjusting the time for which home appliances are used; and (2) aggregating the power demand of households with different attributes. The results showed that these DSMs would contribute to a more stable power system operation. Future research might address the rapid penetration of community energy management systems and demand response systems.

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

“…One of the approaches is clustering-based methods, to extract representative and determinant occupancy or energy use profiles among massive samples [165,172]. Clustering methods are most likely top-down approaches and commonly associate with statistic [170] or probabilistic analysis [174,175].…”

On the Acceptability of Electricity Demand Side Management by Time of Day

“…To confirm the social effectiveness of DSM, Nakano and Washizu [6] calculated the potential for adjusting electricity usage in line with DR, according to the behavioral change of consumers at different time slots, using the National Survey on Time Use and Leisure Activities (STU). STU is a public statistical survey conducted by the Ministry of Internal Affairs and Communications (MIC) of Japan, which shows the daily behavioral patterns of people with various attributes every 15 min.…”

“…STU is a public statistical survey conducted by the Ministry of Internal Affairs and Communications (MIC) of Japan, which shows the daily behavioral patterns of people with various attributes every 15 min. Using STU, Nakano and Washizu [6] calculated the adjustable electricity usage assumed for each 15 min period as follows: For example, suppose a person with a certain attribute cleans house for a certain 15 min period of the day. In that case, assuming that the person was using a vacuum cleaner for 15 min, Nakano and Washizu [6] calculated the adjustable electricity that the person could create by not cleaning during that 15 min period.…”

“…Using STU, Nakano and Washizu [6] calculated the adjustable electricity usage assumed for each 15 min period as follows: For example, suppose a person with a certain attribute cleans house for a certain 15 min period of the day. In that case, assuming that the person was using a vacuum cleaner for 15 min, Nakano and Washizu [6] calculated the adjustable electricity that the person could create by not cleaning during that 15 min period. The adjustable electricity for each time slot is calculated by summing the adjustable electricity for each 15 min, of people with each attribute, weighted by the proportion of the demographic composition by attribute.…”

“…The adjustable electricity for each time slot is calculated by summing the adjustable electricity for each 15 min, of people with each attribute, weighted by the proportion of the demographic composition by attribute. (In addition to calculating the potential power savings for each 15 min period of people using domestic appliances, Nakano and Washizu [6] also calculated the potential power savings in each 15 min period when old domestic appliances are replaced with energy-saving ones and when using domestic appliances with more energy-saving behavior. In this research, we focused on the calculation result when the use of domestic appliances was stopped.…”