A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
Towards a Systematic Survey for Carbon Neutral Data Centers
[article]
2022
arXiv
pre-print
Preserved Fulltext
Other Versions
Furthermore, three key scientific challenges for putting such a framework in place are discussed. Finally, several applications for this framework are presented to demonstrate its enormous potential. ...
On the technological front, we propose achieving carbon-neutral data centers by increasing renewable energy penetration, improving energy efficiency, and boosting energy circulation simultaneously. ...
Previous works on renewable energy forecasting leveraged traditional time series analysis tools such as WCMA [65] . ...
arXiv:2110.09284v3
fatcat:gwlgrioigbedzauyeaopdlxngu
Data-driven bidding strategy for DER aggregator based on gated recurrent unit–enhanced learning particle swarm optimization
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
First, a data-driven forecasting model involving gated recurrent unit-enhanced learning particle swarm optimization (GRU-ELPSO) with improved mutual information (IMI) is employed to model renewables and ...
This study proposes a data-driven bidding strategy framework for a DER aggregator confronted with various uncertainties. ...
In general, time-series forecasting models such as long short-term memory (LSTM) or the gated recurrent unit (GRU) have been adopted in state-ofthe-art studies for short-term load or renewable output forecasting ...
doi:10.1109/access.2021.3076679
fatcat:njaw3so2ajfhvihidb5vky4uaq
Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting
[article]
2023
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
To tackle these issues, we propose a novel framework with two objectives: (i) combating uncertainty of renewable energy in smart grid by leveraging time-series forecasting with Long-Short Term Memory ( ...
LSTM) solutions, and (ii) establishing distributed and dynamic decision-making framework with multi-agent reinforcement learning using Deep Deterministic Policy Gradient (DDPG) algorithm. ...
As a result, this memory cell allows the LSTM to learn longer-term dependencies in the timeseries, and makes it an appropriate choice for time-series forecasting. ...
arXiv:2302.14094v1
fatcat:2tzof76kfjebjofp3ky6x3z6zu
Recurrent Deep Multiagent Q-Learning for Autonomous Brokers in Smart Grid
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The broker mechanism is widely applied to serve for interested parties to derive long-term policies in order to reduce costs or gain profits in smart grid. ...
In this paper, we develop an effective pricing strategy for brokers in local electricity retail market based on recurrent deep multiagent reinforcement learning and sequential clustering. ...
Then it calculates distances between the corresponding points in the order of optimal match rather than in the order of time. ...
doi:10.24963/ijcai.2018/79
dblp:conf/ijcai/YangHSWFS18
fatcat:uel4adcwpbcclevqtm73u6d6ae
Contract-theoretic Demand Response Management in Smart Grid Systems
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
at an announced price, and the prosumers, who offer their "effort" by paying for the purchased electricity. ...
The contract-theoretic DRM problem is formulated as a maximization problem of the electricity market's utility, while jointly guaranteeing the optimal satisfaction of the prosumers, under the scenarios ...
In [24] , a distributed prosumers' utility maximization framework is proposed, where the optimal prices and the demand schedules are determined in order for each prosumer to maximize its net benefit subject ...
doi:10.1109/access.2020.3030195
fatcat:sgktbumivrgujpojqtrn55enra
Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2022 accepted arXiv:2202.03771v2 fatcat:5pvhyhccj5bmteiwbrfi7v6hsq
Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. ...
contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. ...
The stochastic renewable energy and multi-energy demand have a joint impact on system performance. ...
arXiv:2202.03771v1
fatcat:vvohb5g5vvba3ioz3cih6tyeim
Applications of Probabilistic Forecasting in Smart Grids: A Review
2022
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. ...
Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. ...
With the help of the sequential method, uncertainties of time-series inputs (such as variable generation of renewable energy resources and electricity demand) are implemented in a better way [75] . ...
doi:10.3390/app12041823
doaj:7983bd9658584546868ebcb5964433cc
fatcat:ddrmqguhmbe6dpn5nbw3cl43bm
Identification of new forecasting products
2021
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
As the share of renewable energy generation in the energy mix increases, in parallel to a liberalization of electricity markets, new needs for forecasting have appeared. ...
Forecasting of renewable energy generation is a mature R&D area, also with a lot of operational experience and commercial offering. ...
Joint densities would in that case give the complete information about what will happen for each location, time and renewable energy type involved, as well as dependencies. ...
doi:10.5281/zenodo.5836716
fatcat:4bspwlyipvfmzksfkx6cvpapfu
Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The flexible and reliable DC nanogrid is suitable for integrating renewable energy for a distribution system. ...
Consequently, the cooperative P2P power trading system maximizes the profit by considering the time of use (ToU) tariff-based electricity cost and the system marginal price (SMP), and minimizes the amount ...
C.Reinforcement learning The RL deals with knowledge acquisition on how agents should figure out the sequences of actions in a given environment in order to maximize cumulative reward. ...
arXiv:2209.07744v2
fatcat:jiznqw5udfg5hmeuw3rzbou67i
Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty
2022
IEEE Transactions on Smart Grid
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded ...
The structure of multimicrogrids provides the possibility to construct flexible and various energy trading framework. ...
An RO framework for joint an optimal scheduling of energy and reserves for multi-microgrids was proposed in [14] , in which the non-anticipativity in reserve scheduling is considered. ...
doi:10.1109/tsg.2022.3147370
fatcat:chcog3yv7nfvxoa34znuwgrcie
Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. ...
As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems ...
They formulate a joint optimization problem for resource reuse and power allocation for D2D links in order to maximize the energy-efficiency for cellular D2D-based V2X communication network, with the consideration ...
arXiv:2108.11879v1
fatcat:l3fidzspwbhi5mmw55dds7vddu
Challenges in Transition to m Commerce in Rural India
2017
International Journal of Computer Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
An descriptive study to evaluate various kinds of models for different kinds of data distribution is aimed at identifying the best kind of Hidden Markov Model for studying the issue of channel migration ...
India being a predominantly agricultural economy has lot of potential for m commerce marketing. ...
(Zucchini and Macdonald 2009) proposed the most comprehensive Hidden Markov Model theory for time series. ...
doi:10.5120/ijca2017915387
fatcat:wg2lwtqisrhupcbampwigsotpi
Consumer Choice Model for Forecasting Demand and Designing Incentives for Solar Technology
2011
Social Science Research Network
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Using this model, we develop a framework for policy makers to find optimal subsidies in order to achieve a desired adoption target with minimum cost for the system. ...
In particular, we assume consumers purchase these solar panels according to a discrete choice model. ...
Acknowledgments We would like to thank the following people for the private conversations we had, where we obtained great ...
doi:10.2139/ssrn.1748424
fatcat:6dtjknu27ba35bcuyg4uorspm4
Energy Sustainable Mobile Networks via Energy Routing, Learning and Foresighted Optimization
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
., they can also purchase energy from the power grid. ...
is devised for the computation of energy allocation and transfer schedules. ...
This framework is designed for online use and combines learning and foresighted optimization. ...
arXiv:1803.06173v1
fatcat:vmfurebk2jhl5fswyvkdsirhha
An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud-Edge Collaborative Environment
2021
Advances in Civil Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Therefore, this paper proposes an ordered charging scheduling method for EV in the cloud-edge collaborative environment. ...
Firstly, the uncertainty of user load demands, charging station requirements, and renewable outputs are taken into consideration. ...
series used for load curve fitting. ...
doi:10.1155/2021/6690610
fatcat:6qfwwfqkkvazlkwoqwl2myriee
« Previous
Showing results 1 — 15 out of 14,953 results