Peak load optimization through 2-dimensional packing and multi-processor real-time scheduling

Martini, Daniele De; Benetti, Guido; Cipolla, Filippo; Caprino, Davide; Vedova, Marco L. Delia; Facchinetti, Tullio

doi:10.1145/3075564.3075587

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…Moreover, energy storage and optimization are employed to mitigate peak demand [24,25]. Furthermore, linear optimization is applied to optimize peak loads and real-time scheduling algorithms [26], collectively enhancing energy management and reducing peak power consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

Touhs,

Temouden,

Khallaayoun

et al. 2023

WEVJ

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This research delves into the intricate landscape of energy scheduling and optimization within microgrid and residential contexts, addressing pivotal aspects such as real-time scheduling systems, challenges in dynamic pricing, and an array of optimization strategies. This paper introduces a cutting-edge scheduling algorithm, harnessing the power of artificial neural networks driven by Long Short-Term Memory Networks, and highlights its exceptional performance, boasting a significantly lower Mean Absolute Error of 5.32 compared to conventional models. This heightened predictive accuracy translates into tangible improvements in both energy efficiency and cost savings. This study underscores the delicate balance between user satisfaction, cost reduction, and efficient scheduling for sustainable energy consumption, showcasing a remarkable 38% enhancement in optimized schedules. Further granularity revealed substantial gains in energy efficiency and cost reduction across different scheduling intensities: 11.11% in light schedules, 20.09% in medium schedules, and an impressive 38.85% in heavy schedules. However, this research does not shy away from highlighting challenges related to data quality, computational demands, and generalizability. Future research trajectories encompass the development of adaptive models tailored to diverse data qualities, enhancements in scalability for and adaptability to various microgrid configurations, the integration of real-time data, the accommodation of user preferences, the exploration of energy storage and renewables, and an imperative focus on enhancing algorithm transparency.

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Section: Literature Reviewmentioning

confidence: 99%

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

Touhs,

Temouden,

Khallaayoun

et al. 2023

WEVJ

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show abstract

“…Optimization methods are traditionally oriented towards consumption on peak shaving [11], [12] or global reduction by avoiding idle time processes [13]. These methods are relatively easy to apply to self-consumption efficiency scenarios [14] but require adaptation.…”

Section: Related Workmentioning

confidence: 99%

Benefits of Energy Management Systems on local energy efficiency, an agricultural case study

Rio¹,

Maurel²,

Barais³

et al. 2019

2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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Energy efficiency is a concern impacting both ecology and economy. Most approaches aiming at reducing the energy impact of a site focus on only one specific aspect of the ecosystem: appliances, local generation or energy storage. A trade-off analysis of the many factors to consider is challenging and must be supported by tools. This paper proposes a Model-Driven Engineering approach mixing all these concerns into one comprehensive model. This model can then be used to size either local production means, either energy storage capacity and also help to analyze differences between technologies. It also enables process optimization by modeling activity variability: it takes the weather into account to give regular feedback to the end user. This approach is illustrated by simulation using real consumption and local production data from a representative agricultural site. We show its use by: sizing solar panels, by choosing between battery technologies and specification and by evaluating different demand response scenarios while examining the economic sustainability of these choices.

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Real-time cyber/physical interplay in scheduling for peak load optimisation in Cyber–Physical Energy Systems

De Martini,

Benetti,

Facchinetti

2024

Intelligent Systems with Applications

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Peak load optimization through 2-dimensional packing and multi-processor real-time scheduling

Cited by 5 publications

References 3 publications

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

Benefits of Energy Management Systems on local energy efficiency, an agricultural case study

Real-time cyber/physical interplay in scheduling for peak load optimisation in Cyber–Physical Energy Systems

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