A Neural Network-Based Appliance Scheduling Methodology for Smart Homes and Buildings with Multiple Power Sources

Shukla, Raj Mani; Kansakar, Prasanna; Munir, Arslan

doi:10.1109/inis.2016.046

Cited by 8 publications

(8 citation statements)

References 8 publications

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“…Moreover, the energy consumption data can be visualized and analyzed to help the users to minimize the building's energy consumption. This could be achieved by following different schemes like scheduling the use of home appliances based on the peak times of energy consumption . Consequently, residents will be able to have control and management over their electricity consumption that will reduce the cost of bills and minimize the carbon footprint of the building …”

Section: Smart Metering Energy Managementmentioning

confidence: 99%

“…[18][19][20] Smart meters are used to manage energy consumption by controlling the switching of different electric equipment over different times of the day based on energy analysis and loads' profiles. 21 Furthermore, Internet of Things (IoT) has a prime rule in making the monitoring systems in smart buildings into an efficient energy management systems. 14,[22][23][24] Often, smart meters establish WiFi communication to push data to a server and to share data with other meters.…”

Section: Introductionmentioning

confidence: 99%

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Design and realization of an open‐source and modular smart meter

Sayed

Hussain

Gastli

et al. 2019

Energy Science & Engineering

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This paper presents the design of a modular and open‐source smart meter for educational and research purposes. The smart meter monitors the energy consumption, uploads the data to a cloud server, and can be used to control appliances remotely through a control relay. Using the Internet of Things (IoT) technology, the smart meter acts as a sensing node in a meshed network. This enables consumers to visualize their energy consumption profiles at the level of appliances and to take control actions to manage efficient energy consumption using the automation platforms or IoT with smart meters. In this paper, the energy consumed by different electrical appliances is computed and communicated to a server using the Arduino Uno board aided by the ESP8266 WiFi module. The energy consumption is also displayed on an LCD screen, and the consumption profiles are displayed through the home energy monitoring website “EmonCMS.” The design can accommodate different transducer ratings with single‐phase and three‐phase measurements by configuring itself automatically using the plug‐and‐play approach. The realization of the proposed smart meter is a multicourse educational project that aims to polish students’ skills in applying the knowledge gained in Electrical Engineering (ie, undergraduate courses) to produce a practical application.

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Section: Smart Metering Energy Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and realization of an open‐source and modular smart meter

Sayed

Hussain

Gastli

et al. 2019

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…Recently, a lot of insightful works on price drive DR have been proposed to encourage optimal utilization of lower tariffs. Nevertheless, a majority of these works were proposed for single house scenarios [10][11][12][13][14][15][16][17]. A greedy method based appliance scheduler that incorporates neural networks with multiple energy sources has been proposed by Shukla et al [14] to improve energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, a majority of these works were proposed for single house scenarios [10][11][12][13][14][15][16][17]. A greedy method based appliance scheduler that incorporates neural networks with multiple energy sources has been proposed by Shukla et al [14] to improve energy efficiency. Multiple schemes for autonomous appliance scheduling based on price driven DR were proposed by Khan et al and Silva et al, in order to preserve energy, while reducing cost on energy [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Slack Time based Price Driven Demand Response for Future Effectual Smart Communities

Silva,

Khan,

Han

2019

IJEAT

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Evolution of smart grid concept aims to address the imbalance between electricity demand and supply. Owing to consideration on sustainable energy, user comfort, and cost efficiency, residential Demand Response (DR) has gained a remarkable popularity over the past few years. To further enhance these benefits, herein we propose a residential appliance scheduling algorithm inspired by Least Slack Time (LST) algorithm. The conventional LST algorithm is amended with consumption thresholds and waiting factor constraints to derive proposed Minimum Slack Time (MST) algorithm, which increase cost and comfort efficiency during DR. Proposed algorithm was experimented in a simulated residential community consists of 50 houses. Further experiments were conducted by aggregating renewable energy sources using aggregated MST (AMST) algorithm. All instances were compared with an existing scheduling mechanism to assure superiority of proposed MST and AMST algorithms, in terms of grid electricity consumption, cost, Peak-to-Average Ratio (PAR), and waiting time.

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“…In the recent past, multiple efforts were made to reduce the cost of energy by scheduling domestic appliances. A smart home can schedule its devices and appliances to minimize the cost of energy, with the aid of energy price estimation [ 20 ]. In [ 21 ], an autonomous appliance scheduler based on time of use (TOU) and real-time pricing (RTP) was proposed for a single home scenario.…”

Section: Introductionmentioning

confidence: 99%

Load Balancing Integrated Least Slack Time-Based Appliance Scheduling for Smart Home Energy Management

Silva

Khan

Han

2018

Sensors

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The emergence of smart devices and smart appliances has highly favored the realization of the smart home concept. Modern smart home systems handle a wide range of user requirements. Energy management and energy conservation are in the spotlight when deploying sophisticated smart homes. However, the performance of energy management systems is highly influenced by user behaviors and adopted energy management approaches. Appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption. Hence, we propose a smart home energy management system that reduces unnecessary energy consumption by integrating an automated switching off system with load balancing and appliance scheduling algorithm. The load balancing scheme acts according to defined constraints such that the cumulative energy consumption of the household is managed below the defined maximum threshold. The scheduling of appliances adheres to the least slack time (LST) algorithm while considering user comfort during scheduling. The performance of the proposed scheme has been evaluated against an existing energy management scheme through computer simulation. The simulation results have revealed a significant improvement gained through the proposed LST-based energy management scheme in terms of cost of energy, along with reduced domestic energy consumption facilitated by an automated switching off mechanism.

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A Neural Network-Based Appliance Scheduling Methodology for Smart Homes and Buildings with Multiple Power Sources

Cited by 8 publications

References 8 publications

Design and realization of an open‐source and modular smart meter

Design and realization of an open‐source and modular smart meter

Enhanced Slack Time based Price Driven Demand Response for Future Effectual Smart Communities

Load Balancing Integrated Least Slack Time-Based Appliance Scheduling for Smart Home Energy Management

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