Power consumption minimization by distributive particle swarm optimization for luminance control and its parallel implementations

Liao, Chih-Lun; Lee, Shie-Jue; Chiou, Yu-Shu; Lee, Ching-Ran; Lee, Chie-Hong

doi:10.1016/j.eswa.2017.11.002

Cited by 18 publications

(9 citation statements)

References 29 publications

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“…A common approach to deal with this problem includes deploying generation sources along the power line, regulated by an optimal dispatch strategy [60], [75], [76]. 3) Peak load -utility companies encourage customers to minimise peak load demand or even to achieve a particular load profile, which benefits grid management [46], [80], [82], [146], [147]. By defining individual target loads, utility companies promote a balanced use of power grid resources, expressed as peak-to-average load ratio (PALR).…”

Section: B Criteriamentioning

confidence: 99%

A Survey on Home Energy Management

et al. 2020

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Energy is a vital resource for human activities and lifestyle, powering important everyday infrastructures and services. Currently, pollutant and non-renewable sources, such as fossil fuels, remain the main source of worldwide consumed energy. The environmental impact of their exploitation has boosted research and investments in alternative, clean and renewable sources, including photovoltaic and windbased systems. As a whole, buildings are one of the major energy consumption sectors. Hence, improving energy efficiency in buildings will result in economical and environmental gains. In the case of households, home energy management systems are mainly used for monitoring real-time consumption and to schedule appliance operations so that the energy bill could be minimised, or according to another specific criterion. This work aims to survey the most recent literature on home energy management systems, providing an aggregated and unified perspective in the context of residential buildings. In addition, an updated literature list regarding commonly managed household appliances and scheduling objectives are included. Physical and operational constraints, and how they are addressed by home energy management systems along with security issues are also discussed. INDEX TERMS Energy efficiency, home energy management systems, household appliance models, load management, optimal scheduling, smart homes, security.

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Section: B Criteriamentioning

confidence: 99%

A Survey on Home Energy Management

et al. 2020

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“…As noted in Section II, the aforementioned parametrisation of the Poisson probability distributions is provided by the Alliance for Water Efficiency. 4 Concerning non-controllable household electricity demand, it is represented by vector E home , which is also of length T . Each vector element E home i (expressed in kW and with i = 1, .…”

Section: A Parametersmentioning

confidence: 99%

“…Consumption reduction proposes to curtail demand by upgrading to energy-efficient devices and disconnecting unnecessary loads, whereas load shifting is regulated by efficient, less intrusive, and cost-effective consumption shifting policies. The popularity of load shifting in residential buildings is, to some extent, linked to the less expensive and less intrusive intervention mechanisms [4].…”

Section: Introductionmentioning

confidence: 99%

A Compressive Receding Horizon Approach for Smart Home Energy Management

et al. 2021

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“…it is in charge of database readings making use of SparkSQL, of message queues connections using Spark Streaming and of data processing such as training of the learning model and predictions with Spark TS [62]. Although Hadoop has its own executors, we decided to use Spark because, as previously explained, it provides additional useful tools [63] and its processing speed is higher than with Hadoop alone [64], [65].…”

Section: A Prediction Module Architecture and Technologiesmentioning

confidence: 99%

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

et al. 2019

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The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study.

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Power consumption minimization by distributive particle swarm optimization for luminance control and its parallel implementations

Cited by 18 publications

References 29 publications

A Survey on Home Energy Management

A Survey on Home Energy Management

A Compressive Receding Horizon Approach for Smart Home Energy Management

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

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