Modelling of a multi-stage energy management control routine for energy demand forecasting, flexibility, and optimization of smart communities using a Recurrent Neural Network

Petrucci, Andrea; Barone, Giovanni; Buonomano, Annamaria; Athienitis, Andreas

doi:10.1016/j.enconman.2022.115995

Cited by 54 publications

(8 citation statements)

References 81 publications

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“…The Visual Studio Net framework, an expanded version of cloudsim that allows large-scale scheduling, was used to run three algorithms, including DVFS, Shortest Job First, and the Energy management algorithm (EMA), in our experimental findings. By executing two processes from the Visual Studio Net Framework [34] (Montage and CyberShake) and increasing the number of VMs used, we were able to calculate the Makespan of each workflow (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). We utilized the formula given in to get the Makespan.…”

Section: Energy Managementmentioning

confidence: 99%

“…Accessibility and types of cloud. The suggested method includes two stages: the first stage assists in achieving work deadlines and cutting down on execution time without taking energy consumption into account; task redistribution scheduling chooses the least energy-intensive site for execution while staying [6] within the deadline. Additionally, we recommend an energy-efficient mechanism for prioritizing tasks to achieve a favorable balance between energy conservation and job scheduling.…”

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confidence: 99%

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An Efficient Task Scheduling for Cloud Computing Platforms Using Energy Management Algorithm: A Comparative Analysis of Workflow Execution Time

Ahmed,

Adnan,

Abdullah

et al. 2024

IEEE Access

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Cloud computing platform offers numerous applications and resources such as data storage, databases, and network building. However, efficient task scheduling is crucial for maximizing the overall execution time. In this study, workflows are used as datasets to compare scheduling algorithms, including Shortest Job First, First Come, First Served, (DVFS) and Energy Management Algorithms (EMA). To facilitate comparison, the number of virtual machines in the Visual Studio Net framework environment is increased. The experimental findings indicate that increasing the number of virtual machines reduces Makespan. Moreover, the Energy Management Algorithm (EMA) outperforms Shortest Job First by 2.79% for the CyberShake process and surpasses the First Come, First Serve algorithm by 12.28%. Additionally, EMA produces 21.88% better results than both algorithms combined. For the Montage process, EMA performs 4.50% better than Shortest Job First and 25.75% superior to the First Come, First Serve policy. Finally, we ran simulations to determine the performance of the suggested mechanism and contrasted it with the widely used energy-efficient techniques. The simulation results demonstrate that the suggested structural design may successfully reduce the amount of data and give suitable scheduling to the cloud. INDEX TERMS Energy Management Algorithm (EMA); First come First Serve (DVFS); Shortest Job First (RR); Makespan; VMs I. INTRODUCTION

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Section: Energy Managementmentioning

confidence: 99%

mentioning

confidence: 99%

An Efficient Task Scheduling for Cloud Computing Platforms Using Energy Management Algorithm: A Comparative Analysis of Workflow Execution Time

Ahmed,

Adnan,

Abdullah

et al. 2024

IEEE Access

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“…At present, the development of marine diesel engines in carbon neutrality and air pollution reduction is crucial. Energy management and cost reduction across a variety of economic sectors could significantly improve with the implementation of energy efficiency technologies and smart control tactics [11]. 46% of total CO2 emissions are attributable to the transportation and industry sectors, with the shipbuilding sector accounting for 11% of the latter [12].…”

Section: Vesselsmentioning

confidence: 99%

Research on technological innovation and application of modern diesel engines

Zhang

2023

TNS

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Diesel engines have been instrumental in driving industrial advancement. This paper provides an in-depth analysis of the evolution and application of diesel engine technology across various sectors, set against the backdrop of the global energy crisis and swift technological progress. The research emphasizes technological breakthroughs like turbocharging and common rail fuel injection, which have been pivotal in augmenting combustion efficiency and reducing environmental impact. The potential of alternative fuels, such as biodiesel, synthetic diesel, and water-emulsified diesel, in curbing emissions and bolstering combustion efficiency is also critically assessed. The diverse applications of diesel engines, spanning marine vessels to aviation, are explored, highlighting the adaptability of these engines to evolving environmental norms and market needs. The paper further investigates strategies to enhance thermal efficiency and performance, spotlighting innovative combustion methods like HCCI and RCCI. The pivotal role of accurate fuel injection control and injector design in refining combustion quality is also accentuated. In summation, while the study underscores the persistent relevance of diesel engine technology amidst modern challenges, it also advocates for sustained innovation to harmonize performance with environmental concerns. The prospective trajectory for diesel engines might encompass an amalgamation of traditional and novel energy solutions to address shifting energy and emission standards.

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“…In [6], a Stackelberg game optimization model considering a carbon trading mechanism and IDR for CIES was developed, and the effect of diverse user aggregator engagement levels on CIES was comprehensively analysed. An innovative algorithm using a recurrent neural network was proposed for CIES energy management control to optimize energy flows [7].…”

Section: Introductionmentioning

confidence: 99%

A Stochastic MPC-Based Flexibility Scheduling Strategy for Community Integrated Energy System Considering Multi-Temporal-Spatial-Scale and Inertia Components

Zhang,

2024

Processes

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The network trend of isolated communities adds urgency to accelerate the deployment of community integrated energy systems (CIES). CIES effectively combines and optimizes multiple energy systems, leveraging their complementarity for efficient utilization and economical energy supply. However, the escalating intricacies of coupling multiple energy sources and the rising system uncertainties both pose challenges to flexibility scheduling of energy supply and demand. Additionally, the potential flexibility of building thermal inertia and pipeline gas linepack in diverse CIES, encompassing residential, commercial, and industrial communities, remains unexplored. To tackle these issues, a stochastic model predictive control (SMPC) based multi-temporal-spatial-scale flexibility scheduling strategy considering multiple uncertainty sources and system inertia components is proposed. First, the optimization model of CIES is formulated to improve operational flexibility and efficiency, resolve energy discrepancies and expand the capacity for renewable energy utilization. Then, the SMPC-based framework embedding an auto-regressive model and scenario generation method are established to make real-time corrections to the day-ahead scheduling stage and offset the prediction errors of uncertainty sources economically. Furthermore, thermal inertia of the aggregated buildings with different envelopes and linepack in gas pipelines are both leveraged to enhance the flexibility and synergy of CIES. Finally, a case study is executed to verify the effectiveness and applicability of the proposed strategy. The simulation results unequivocally demonstrate that this strategy successfully coordinates and harnesses complementary advantages from various energy sources, fostering a balanced energy supply-demand equilibrium across multiple temporal and spatial scales.

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Modelling of a multi-stage energy management control routine for energy demand forecasting, flexibility, and optimization of smart communities using a Recurrent Neural Network

Cited by 54 publications

References 81 publications

An Efficient Task Scheduling for Cloud Computing Platforms Using Energy Management Algorithm: A Comparative Analysis of Workflow Execution Time

An Efficient Task Scheduling for Cloud Computing Platforms Using Energy Management Algorithm: A Comparative Analysis of Workflow Execution Time

Research on technological innovation and application of modern diesel engines

A Stochastic MPC-Based Flexibility Scheduling Strategy for Community Integrated Energy System Considering Multi-Temporal-Spatial-Scale and Inertia Components

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