Smart grid real‐time pricing optimization management on power consumption of building multi‐type air‐conditioners

Morikawa, Junji; Yamaguchi, Takayuki; Ninagawa, Chuzo

doi:10.1002/tee.22308

Cited by 17 publications

(14 citation statements)

References 5 publications

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“…The objective of this research was to compare our new method of training data collection with conventional methods [1][2][3]. Therefore, we decided to use a simple three-layered neural network a Correspondence to: Chuzo Ninagawa.…”

Section: Neural Network Modelmentioning

confidence: 99%

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Modulation training method of prediction model for smart grid FastADR power limitation of building air‐conditioners

Ninagawa

Nakamura

Morikawa

2017

IEEJ Transactions Elec Engng

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Fast automated demand response (FastADR) of building air-conditioners is a future smart grid demand-side technology. Neural network models will be useful to predict the power limitation result of the FastADR. However, neural networks require training data, which are collected by the experimental air-conditioning operations. In this letter, we propose a new training data collection method in which the FastADR-like signal is modulated into the normal air-conditioning operations.

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Section: Neural Network Modelmentioning

confidence: 99%

“…Multi-type building air-conditioners are promising resources for FastADR. For the prediction of achievable FastADR power reduction, a neural network model could be used [1,2]. However, training experiments are necessary, and they sometimes disturb the building residents.…”

Section: Introductionmentioning

confidence: 99%

Modulation training method of prediction model for smart grid FastADR power limitation of building air‐conditioners

Ninagawa

Nakamura

Morikawa

2017

IEEJ Transactions Elec Engng

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“…In the literature, several advanced technical optimization approaches that deal with residential DR for smart homes in a smart grid have been developed, where (1) heuristic-based load control strategies for diverting peak power consumption [ 6 ] and shedding household appliances [ 7 ] and (2) load-scheduling methods for scheduling power consumption on household appliances [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] have been proposed. The authors have attempted to address residential DR.…”

Section: Introductionmentioning

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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“…FastADR (Fast Automated Demand Response) is explored as a smart grid technology in which consumers respond to power control commands on the minute level. [1][2][3][4][5][6][7] Multitype building air-conditioners are considered promising as consumer equipment for FastADR. This is explained by their high penetration rate and controllability; on the other hand, complicated refrigerant circuits require distributed embedded control, which involves stochastic control operations, and may cause uncertainties when response is needed within several minutes.…”

Section: Introductionmentioning

confidence: 99%

Estimation on Averaging Effect of Large-scale Aggregation of DR Transient Responses of Building Multi Air-conditioning Facilities

et al. 2018

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Building-multitype packaged air-conditioners are expected to be possible resources of Fast Automated Demand Response (FastADR). However, transient response behaviors of each air-conditioner are stochastic due to complicated refrigerant embedded controls. Although ensemble averaging effect is expected in aggregation of a large number of the air-conditioners, it is difficult to carry out experiments with many actual office buildings. In this research, repetitive response time-series data were collected from several air-conditioners. We discuss the possibility of estimation on the ensemble averaging effects using the repetitive response data. K E Y W O R D Sfast automated demand response, multitype building air-conditioner 22

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Smart grid real‐time pricing optimization management on power consumption of building multi‐type air‐conditioners

Cited by 17 publications

References 5 publications

Modulation training method of prediction model for smart grid FastADR power limitation of building air‐conditioners

Modulation training method of prediction model for smart grid FastADR power limitation of building air‐conditioners

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

Estimation on Averaging Effect of Large-scale Aggregation of DR Transient Responses of Building Multi Air-conditioning Facilities

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