Embedded platform for Web-based monitoring and control of a smart home

Moga, Daniel; Stroia, Nicoleta; Petreuş, Dorin; Moga, Rozica; Munteanu, Radu

doi:10.1109/eeeic.2015.7165349

Cited by 6 publications

(4 citation statements)

References 5 publications

(3 reference statements)

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“…This architecture was already validated as a distributed monitoring and control solution for building management systems (Figure 8 [45]), paving the road for an integrated smart home architecture (Figure 9) that encompasses all the implied areas (energy management, HVAC control, access control, entertainment, non-intrusive user detection, etc.). The growing demand on data-storage platforms favors the use of cloud computing and virtualization technologies [46].…”

Section: The Proposed Sensing and Control Architecturementioning

confidence: 99%

“…Considering previously developed sensing modules (Figure 10) ( [45,[50][51][52][53]), and the versatile modular architecture that was demonstrated in a broad range of applications (greenhouse climate control, smart-home on-demand ventilation, air-quality monitoring, etc. ), the first approach considered was to reuse the hardware functionality of the host module, based on a 32-bit microcontroller, as much as possible, and to interface it to the energy metering board.…”

Section: Development Of the Smart-meter Node With Wireless Communicationmentioning

confidence: 99%

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Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters

Stroia

Moga²,

Petreuş³

et al. 2022

Buildings

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The monitoring of power consumption and the forecasting of load profiles for residential appliances are essential aspects of the control of energy savings/exchanges at multiple hierarchical levels: house, house cluster, neighborhood, and city. External environmental factors (weather conditions) and inhabitants’ behavior influence power consumption, and their usage as part of forecasting activity may lead to added value in the estimation of daily-load profiles. This paper proposes a distributed sensing infrastructure for supporting the following tasks: the monitoring of appliances’ power consumption, the monitoring of environmental parameters, the generation of records for a database that can be used for both identifying load models and testing load-scheduling algorithms, and the real-time acquisition of consumption data. The hardware/software codesign of an integrated architecture that can combine the typical distributed sensing and control networks present in modern buildings (targeting user comfort) with energy-monitoring and management systems is presented. Methods for generating simplified piecewise linear (PWL) representations of the load profiles based on these records are introduced and their benefits compared with classic averaged representations are demonstrated for the case of peak-shaving strategies. The proposed approach is validated through implementing and testing a smart-meter node with wireless communication and other wired/wireless embedded modules, enabling the tight integration of the energy-monitoring system into smart-home/building-automation systems. The ability of this node to process power measurements with a programable granularity level (seconds/minutes/hours) at the edge level and stream the processed measurement results at the selected granularity to the cloud is identified as a valuable feature for a large range of applications (model identification, power saving, prediction).

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Section: The Proposed Sensing and Control Architecturementioning

confidence: 99%

Section: Development Of the Smart-meter Node With Wireless Communicationmentioning

confidence: 99%

Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters

Stroia

Moga²,

Petreuş³

et al. 2022

Buildings

Self Cite

View full text Add to dashboard Cite

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“…It is assumed that each EV owner travels 5.6 km daily according to [62], which is the average daily travelling distance in Greece. It is calculated that the energy consumption due to the daily transportation is equal to 918.4 Wh according to (23), where DC EV i is the daily consumption of an EV, d EV i is the distance it travels and c EV i is its consumption per kilometer. The overall daily travelling distance of each EV is assumed to be divided in two equal parts, in order for the EV owners to arrive and to leave their workplace respectively.…”

Section: Case Studymentioning

confidence: 99%

“…Of note, this sort of environment also promotes the incorporation of more recent, advanced technologies in the overall monitoring and control system, such as the internet of things (IoT), home energy management, etc. [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Day Ahead Optimal Dispatch Schedule in a Smart Grid Containing Distributed Energy Resources and Electric Vehicles

Fotopoulou

Rakopoulos

Blanas

2021

Sensors

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This paper presents a day ahead optimal dispatch method for smart grids including two-axis tracking photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS) and electric vehicles (EV), which serve as additional storage systems in vehicle to grid (V2G) mode. The aim of the day ahead schedule is the minimization of fuel-based energy, imported from the main grid. The feasibility of the proposed method lies on the extensive communication network of the smart grids, including sensors and metering devices, that provide valuable information regarding the production of the distributed energy resources (DER), the energy consumption and the behavior of EV users. The day ahead optimal dispatch method is applied on a smart grid in order to showcase its effectiveness in terms of sustainability, full exploitation of DER production and ability of EVs to act as prosumers.

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