An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes

Rocha, Helder R. O.; Honorato, Icaro Henrique; Fiorotti, Rodrigo; Celeste, Wanderley Cardoso; Silvestre, Leonardo J.; Silva, Jair A. L.

doi:10.1016/j.apenergy.2020.116145

Cited by 130 publications

(45 citation statements)

References 43 publications

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“…Reference [12] introduces multi-agent energy management that specifically controls the supply side (producers) in the presence of high penetration of renewable energy sources (RES) and electric vehicles (EV). References [13][14][15][16][17][18] have also surveyed various control strategies of distributed generation to energy management. Researchers in [13] discussed a review of energy management methods.…”

Section: Energy Management In Distribution Systemsmentioning

confidence: 99%

Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

et al. 2021

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During the last few years, attention has overwhelmingly focused on the integrated management of urban services and the demand of customers for locally-based supply. The rapid growth in developing smart measuring devices has made the underlying systems more observable and controllable. This exclusive feature has led the system designers to pursue the implementation of complex protocols to provide faster services based on data exchanges. On the other hand, the demands of consumers for locally-based supply could cause a disjunction and islanding behavior that demands to be dealt with by precise action. At first, keeping a centralization scheme was the main priority. However, the advent of distributed systems opened up new solutions. The operation of distributed systems requires the implementation of strong communication links to boost the existing infrastructure via smart control and supervision, which requires a foundation and effective investigations. Hence, necessary actions need to be taken to frustrate any disruptive penetrations into the system while simultaneously benefiting from the advantages of the proposed smart platform. This research addresses the detection of false data injection attacks (FDIA) in energy hub systems. Initially, a multi-hub system both in the presence of a microgrid (the interconnected smart energy hub-based microgrid system) and without it has been modeled for energy management in a way that allows them to cooperate toward providing energy with each other. Afterward, an FDIA is separately exerted to all three parts of the energy carrier including the thermal, water, and electric systems. In the absence of FDIA detection, the impact of FDIA is thoroughly illustrated on energy management, which considerably contributes to non-optimal operation. In the same vein, the intelligent priority selection based reinforcement learning (IPS-RL) method is proposed for FDIA detection. In order to model the uncertainty effects, the unscented transformation (UT) is applied in a stochastic framework. The results on the IEEE standard test system validate the system’s performance.

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Section: Energy Management In Distribution Systemsmentioning

confidence: 99%

Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

et al. 2021

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“…The battery charging and discharging ratios are evaluated in (19) as the ratio between battery charging or discharging power at slot i of the horizon (i = 1, . .…”

Section: Objectivementioning

confidence: 99%

“…These approaches rely on high-level sampling procedures and sacrifice accuracy, precision and optimality in favour of speed. DA allocation remains the norm, with genetic algorithms [18], [19] and swarm intelligence-based techniques representing common choices. Particle swarm optimization is the most popular swarm-based heuristic [5], [20]- [22], and other include artificial bee colonies [23], polar bears [8], grey wolf optimization [24] and differential evolution [25].…”

Section: Introductionmentioning

confidence: 99%

A Compressive Receding Horizon Approach for Smart Home Energy Management

et al. 2021

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“…And a robust fuzzy multi-objective optimization approach is proposed to determine dynamic pricing decisions under uncertain demand. A multi-objective optimization model considering demand-side management was proposed in Rocha et al (2021). The author considers real-time electricity prices, the priorities of equipment, operating cycles, and energy storage in demand-side management.…”

Section: Structure and Components Of Dcngmentioning

confidence: 99%

Review of Control Strategies for DC Nano-Grid

2021

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As an emerging energy management technology, the DC nano-grid coordinates renewable energy sources output through demand-side management which would provide more options and flexibility for the dispatch of smart buildings and communities with high reliability and efficiency. In this context, this article has analyzed the structure and components of the DC nano-grid. The role and components in DC nano-grid are reviewed in this article. Then the crucial control technologies for the DC nano-grid in recent years are investigated from two aspects: local control and coordinated control, which contains control schemes such as voltage/current control technology, power-sharing technology and cooperative control technology. Different control strategies at various levels are compared and their application scenarios, advantages and disadvantages are also analyzed. The current research progress and challenges are summarized at the end of this article.

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An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes

Cited by 130 publications

References 43 publications

Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

A Compressive Receding Horizon Approach for Smart Home Energy Management

Review of Control Strategies for DC Nano-Grid

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