Analysis of measured data on energy demand and activity patterns in residential dwellings in Japan

Aki, Hirohisa; Iitaka, Hiroshi; Tamura, Itaru; Kegasa, Akeshi; Hayakawa, Hideki; Ishikawa, Yoshiro; Yamamoto, Shigeo; Sugimoto, Ichiro

doi:10.1002/tee.22509

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…The result shows the evidence of the proposed constrained PSO-based residential consumer-centric DSM method considering comfort satisfaction of using electricity to consumers based on the past trends of gathered load data in a smart home environment. Energy demands in residential dwellings are related to activity patterns of residents [ 29 ]. Appliances modeled in Equation (6) and managed by the residential consumer-centric DSM model in this paper can be further analyzed as activity patterns for human life-pattern identification.…”

Section: Case Studymentioning

confidence: 99%

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

Lin

2018

Sensors

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The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

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Section: Case Studymentioning

confidence: 99%

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

Lin

2018

Sensors

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“…The second category used a frequency domain and building information modeling (BIM) to study the residential dwelling load profile. In [24], the authors drew the load patterns of residential dwellings in Japan using BIM software, to control the hot-water operation for 24 h. Linear relationship characteristics have been obtained between the supply and demand for electricity. In [25], the authors used the frequency domain analysis of phase current signals of non-intrusive monitoring systems to study the behaviour of residential dwelling for a day.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Residential Neighborhoods’ Electricity Demand in Iraq Distribution Power Grids

et al. 2021

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In Baghdad City's distribution power grid, a massive number of 630 kV distribution transformers (DTs) are used in residential neighborhoods. Each DT is joined to nine low-voltage 0.415 kV distribution feeders. Each feeder has a designated size of 1 × 240 mm 2 and is joined to a specified number of residential dwellings (= 30) fixed in the initial design stage. The size and number of low-voltage 0.415 kV distribution feeders are set with no change. In this investigation, we use a new approach for modeling electricity demand in residential neighborhoods in Baghdad City and overcome this constraint by finding the optimum number of residential dwellings joined to the same low-voltage 0.415 kV distribution feeder. Two sets of the experimental equations are created to compute the number of residential dwellings that are required to be joined to the low-voltage 0.415 kV distribution feeder. The multi-gradient particle swarm optimization algorithm is used as an optimization tool to handle these experimental equations. Results show that each low-voltage 0.415 kV distribution feeder can be loaded with 50 dwellings instead of 30 due to the diversity among residential dwellings. Several facts about the load profile characteristics of

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“…Therefore, AC usage in the residential area gets a lot of notice as the consumption has made a drastic increase from 6.5 % in 1990 to 16 and also air pollution. It also has been stated that more than 50% of total electrical energy is used for AC and refrigerator [3,8]. The high power of AC consumption causes fluctuations in energy demand and has to be reduced to increase the stability of energy demand on the grid [4].…”

Section: Introductionmentioning

confidence: 99%

Load and Household Profiles Analysis for Air-Conditioning and Total Electricity in Malaysia

Hisham

Zaki

Hagishima

et al. 2019

KSS

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Load profile of household air-conditioning (AC) and total electricity consumption is essential to increase the stability of the energy demand on the grid. Therefore, field measurements on time series data of total and AC electricity consumption from 20 households were conducted from March 2016 to August 2017. The questionnaire survey was carried out simultaneously to grasp the profile of each family. The average total daily and AC consumption were 14.5 kWh/day and 3.9 kWh/day, respectively. The average hourly electricity consumption for total was 0.6 kWh/hour, meanwhile for AC was 0.2 kWh/hour. About 20% of the total peak demand was contributed by the consumption of AC. The indoor air temperature was measured in the bedroom (BR) when AC was switched ON and OFF with an average of 27 ∘C and 29 ∘C, respectively. However, the indoor air temperature in the living room (LR) was 2∘C and 1∘C higher if compared to BR for both conditions. Based on the questionnaire survey, 92% of the occupants preferred a temperature setting below than the level recommended by the Malaysian standard i.e., 24 ∘C. These results might be beneficial to understand the occupant behavior of electricity demand in Malaysia for designing smart grid energy systems in the future.

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Analysis of measured data on energy demand and activity patterns in residential dwellings in Japan

Cited by 9 publications

References 22 publications

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

A Novel Approach for Residential Neighborhoods’ Electricity Demand in Iraq Distribution Power Grids

Load and Household Profiles Analysis for Air-Conditioning and Total Electricity in Malaysia

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