13,044 research outputs found
Smart scheduling for saving energy in grid computing
Energy saving involves two direct benefits: sustainability and cost reduction. Within the field of Informa tion Technology, clusters, grids and data centres represent the hungriest consumers of energy and there fore energy (saving) policies for these infrastructures should be applied in order to maximize their
resources. It is proved in this paper that approximately 40% of energy can be saved in a data centre if
an adequate policy is applied. Furthermore, a software tool is presented where simulations can be run
and results for real scenarios can be obtained.Ministerio de Ciencia e Innovación TIN2009–14378-C02–0
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Extending Demand Response to Tenants in Cloud Data Centers via Non-intrusive Workload Flexibility Pricing
Participating in demand response programs is a promising tool for reducing
energy costs in data centers by modulating energy consumption. Towards this
end, data centers can employ a rich set of resource management knobs, such as
workload shifting and dynamic server provisioning. Nonetheless, these knobs may
not be readily available in a cloud data center (CDC) that serves cloud
tenants/users, because workloads in CDCs are managed by tenants themselves who
are typically charged based on a usage-based or flat-rate pricing and often
have no incentive to cooperate with the CDC operator for demand response and
cost saving. Towards breaking such "split incentive" hurdle, a few recent
studies have tried market-based mechanisms, such as dynamic pricing, inside
CDCs. However, such mechanisms often rely on complex designs that are hard to
implement and difficult to cope with by tenants. To address this limitation, we
propose a novel incentive mechanism that is not dynamic, i.e., it keeps pricing
for cloud resources unchanged for a long period. While it charges tenants based
on a Usage-based Pricing (UP) as used by today's major cloud operators, it
rewards tenants proportionally based on the time length that tenants set as
deadlines for completing their workloads. This new mechanism is called
Usage-based Pricing with Monetary Reward (UPMR). We demonstrate the
effectiveness of UPMR both analytically and empirically. We show that UPMR can
reduce the CDC operator's energy cost by 12.9% while increasing its profit by
4.9%, compared to the state-of-the-art approaches used by today's CDC operators
to charge their tenants
Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management
As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe
NILM techniques for intelligent home energy management and ambient assisted living: a review
The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora:
Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve
01/SAICT/2018/39578
Fundação para a Ciência e Tecnologia through IDMEC, under LAETA:
SFRH/BSAB/142998/2018
SFRH/BSAB/142997/2018
UID/EMS/50022/2019
Junta de Comunidades de Castilla-La-Mancha, Spain:
SBPLY/17/180501/000392
Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project):
TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio
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