Efficient Energy Management Using Adaptive Reinforcement Learning-Based Scheduling in Large-Scale Distributed Systems

This chapter documents a green energy management framework for software development companies. An initial framework has been constructed through analyzing the reports of large scale software development firms. The key components of the framework consist of energy sources and energy efficiency, energy-efficient heating, energy-efficient lighting, and energy-efficient cooling. These themes include a number of sub-themes and criteria therein which are used to build the green energy management framework and then utilized for constructing the research questions for further data collection. The results highlight the most efficient energy sources, efficient heating measures and technologies, efficient lighting measures, and technologies as well as efficient cooling measures and technologies. Implications for practice have been suggested at the end of the chapter.

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Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Deep Reinforcement Learning Framework

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/89 ◽

2019 ◽

Cited By ~ 7

Author(s):

Yaodong Yang ◽

Jianye Hao ◽

Yan Zheng ◽

Chao Yu

Keyword(s):

Reinforcement Learning ◽

Energy Management ◽

Smart Grids ◽

Large Scale ◽

Energy Generation ◽

Distributed Energy ◽

Electricity Cost ◽

Home Energy Management ◽

Distributed Energy Generation ◽

And Storage

Smart grids are contributing to the demand-side management by integrating electronic equipment, distributed energy generation and storage and advanced meters and controllers. With the increasing adoption of electric vehicles and distributed energy generation and storage systems, residential energy management is drawing more and more attention, which is regarded as being critical to demand-supply balancing and peak load reduction. In this paper, we focus on a microgrid scenario in which modern homes interact together under a large-scale setting to better optimize their electricity cost. We first make households form a group with an economic stimulus. Then we formulate the energy expense optimization problem of the household community as a multi-agent coordination problem and present an Entropy-Based Collective Multiagent Deep Reinforcement Learning (EB-C-MADRL) framework to address it. Experiments with various real-world data demonstrate that EB-C-MADRL can reduce both the long-term group power consumption cost and daily peak demand effectively compared with existing approaches.

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A Green Energy Management Framework for Software Development Firms

Emerging Economic Models for Global Sustainability and Social Development - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-5225-5787-6.ch017 ◽

2019 ◽

pp. 312-328

Author(s):

Arunasalam Sambhanthan

Keyword(s):

Software Development ◽

Energy Management ◽

Energy Efficient ◽

Large Scale ◽

Green Energy ◽

Energy Sources ◽

Management Framework ◽

Efficient Energy ◽

Research Questions ◽

Initial Framework

This chapter documents a green energy management framework for software development companies. An initial framework has been constructed through analyzing the reports of large scale software development firms. The key components of the framework consist of energy sources and energy efficiency, energy-efficient heating, energy-efficient lighting, and energy-efficient cooling. These themes include a number of sub-themes and criteria therein which are used to build the green energy management framework and then utilized for constructing the research questions for further data collection. The results highlight the most efficient energy sources, efficient heating measures and technologies, efficient lighting measures, and technologies as well as efficient cooling measures and technologies. Implications for practice have been suggested at the end of the chapter.

Download Full-text