No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Optimization of Renewable Energy Big Data Transactions Based on Vector Evaluated Genetic Algorithm
... In the field of renewable energies several relevant research state-of-the-art projects have being conducted to improve efficiency and to rationalize installation costs by introducing several innovative approaches [2], [3], [4] [5], [6], [7], [8], [9] and [10]. All the described goals and innovation achievements supported by data networks for data flow brought along the new introduction of concepts such as Big Data [11], [12] and [13], Artificial Intelligence (AI) [14], [15], [16], [17], [18], Internet of Things (IoT) [19], [20], [21], [22] and [23] and many others. All these new technologies open the hot topic of cybersecurity into the REP world. ...
In recent years several efforts have being made in bringing smart network connectivity to the Renewable Energy Plant (REP) environment. On the other hand, REP is extending in scale from specialized points where the energy provider acts as a supplier to home REP (self-energy producers). This enables new important features such as: process automation, monitoring, control and optimizations. On the other hand, and in particular during and after the Covid19 pandemics the cybersecurity menace is a massive concern. The digital literacy of a worker of such an infrastructure is relevant to the correct implementation of adequate security policies. This article describes the threats and challenges on the field and conducts an enquire for perceiving the awareness of Automation and Mechanics Engineering students for this relevant problem, as future player in the field.
... In fact, a constraints optimization problem converts to the unconstraint multi-objective problem in the penalty function method. In following this procedure is expressed [107]: ...
Community Microgrid offers effective energy harvesting from distributed energy resources and efficient energy consumption by employing an energy management system (EMS). Therefore, the collaborative microgrids are essentially required to apply an EMS, underlying an operative control strategy in order to provide an efficient system. An EMS is apt to optimize the operation of microgrids from several points of view. Optimal production planning, optimal demand-side management, fuel and emission constraints, and revenue of trading spinning and non-spinning reserve energy can effectively be managed by EMS. Consequently, the importance of optimization is explicit in microgrid applications. In this paper, the most common control strategies in the microgrid community with potential pros and cons are analyzed. Moreover, a comprehensive review of single objective and multi-objective optimization methods is performed by considering the practical and technical constraints, uncertainty, and intermittency of renewable energies sources. The Pareto-optimal solution as the most popular optimization method is investigated in multiple advanced multi-objective optimization algorithms. Eventually, feature selection and neural network-based clustering algorithms in order to analyze the Pareto-optimal set are introduced.
... Big data analytics are not used only for ships but also in energy trade systems. He et al. [12] studied an optimization for increasing the share of electricity generated from renewable energy by using Vector Evaluated Genetic Algorithm (VEGA) by achieving 0.5 of crossover probability, 0.1 of mutation rate of genetic algorithm with a maximum 50 iterations. Yuan and Victor [13] predicted energy consumption estimation based on speed and trim angle of a cargo ship by the Gaussian process model RMSE where the rate for training data was 0.3859 and the rate for validation data was 0.4418. ...
Shipbuilding and shipping areas are technically dependent to each other since shipping is the end user in the shipbuilding industry. One of the biggest maritime challenges arose due to regulations about emissions and has begun to be a worldwide concern. The maritime industry has enormous potential in terms of a reduction in energy use and emission supervision, especially with respect to existing ships. The specific challenges to shipping are energy inefficiency and emissions; vessel electrification, where sailing displacements and infrastructures permit; and vessel performance optimization. One of these challenges is addressed in this work: operational improvement in detection of the impact of fouling or opposite hull cleaning which enables greater efficiency and an increase in performance. In this paper, battery hybrid electric ship automation data are analysed using Curve Fitting and Detrended Fluctuation Analysis (DFA) in order to interpret the impact of fouling on ship energy performance degradation by continuous monitoring. Although this methodology is already used for analysing various time series data, DFA is a novel approach that is first time that applied to a marine based data where fouling contains marine biological properties. Application of these techniques reveal that in only 9 months fouling, performance degradation of case study ferry faced 6% of average speed loss.
The demand of Unmanned Aerial Vehicle (UAV) to monitor natural disasters extends its use to multiple civil missions. While the use of remotely control UAV reduces the human casualties' rates in hazardous environments, it is reported that most of UAV accidents are caused by human factor errors. In order to automate UAVs, several approaches to path planning for UAVs, mainly based on Genetic Algorithm (GA), have been proposed. However, none of the proposed paradigms optimally solve the path planning problem with contrasting objectives. We are proposing a Master-Slave Parallel Vector-Evaluated Genetic Algorithm (MSPVEGA) to solve the path planning problem. MSPVEGA takes advantage of the advanced computational capabilities to process multiple GAs concurrently. In our present experimental set-up, the MSPVEGA gives optimal results for UAV.
This paper serves as an introduction to the Analytic Hierarchy Process - A multicriteria decision making approach in which factors are arranged in a hierarchic structure. The principles and the philosophy of the theory are summarized giving general background information of the type of measurement utilized, its properties and applications.
One of the key advances in resolving the big-data problem has been the
emergence of an alternative database technology. Today, classic RDBMS are
complemented by a rich set of alternative Data Management Systems (DMS)
specially designed to handle the volume, variety, velocity and variability
ofBig Data collections; these DMS include NoSQL, NewSQL and Search-based
systems. NewSQL is a class of modern relational database management systems
(RDBMS) that provide the same scalable performance of NoSQL systems for online
transaction processing (OLTP) read-write workloads while still maintaining the
ACID guarantees of a traditional database system. This paper discusses about
NewSQL data management system; and compares with NoSQL and with traditional
database system. This paper covers architecture, characteristics,
classification of NewSQL databases for online transaction processing (OLTP) for
Big data management. It also provides the list ofpopular NoSQL as well as
NewSQL databases in separate categorized tables. This paper compares SQL based
RDBMS, NoSQL and NewSQL databases with set of metrics; as well as, addressed
some research issues ofNoSQL and NewSQL.
The big data trend is generating compute-intensive and data-intensive applications requiring unique services that are different from conventional computing services. Therefore, there is a need to fundamentally address such requirements by developing market mechanisms for managing, trading, and pricing big data computing services. The cloud computing platforms have a great potential to meet the economic requirements of market mechanisms for big data applications due to their technological advances, cost benefit ratios, and easy to use services. We design a two-sided mechanism for trading computing resources for big data applications. Our proposed mechanism is universally strategy-proof, providing incentives for both cloud providers and users to voluntarily reveal their true private information. We perform extensive experiments to evaluate our proposed mechanism.
The process planning and scheduling (PPS) is to determine a solution (schedule), which tells a production facility what to make, when, and on which equipment, to process a set of parts with operations effectively. Multiobjective PPS problems become more complex because the decision maker need to make a trade-off between two or more objectives while determining a set of optimal nondominated solutions effectively. The previous research works use evolutionary algorithms (EA) to solve such problems, however, the proposed approaches cannot get a good balance between efficacy and efficiency. This paper proposed an improved vector evaluated genetic algorithm with archive (iVEGA-A) mechanism to deal with PPS problem while considering the minimization of the makespan and minimization of the variation of workload of machine. The proposed algorithm has been compared with other approaches to verify and benchmark the optimization reliability on PPS problems. These comparisons indicate iVEGA-A is better than vector evaluated genetic algorithm (VEGA) did on efficacy and negligible difference on efficiency. The efficacy is not less than some famous approaches, such as, nondominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm 2 (SPEA2) and the efficiency is obviously better than the latter.
Construction projects often fail to achieve their time, budget,
and quality goals. This is frequently due to the failure of the
contractor to analyze and assess all risk factors. The analytic
hierarchy process (AHP) is an approach that can be used to analyze and
assess project risks during the bidding stage of a construction project
and to overcome the limitations of the approaches currently used by
contractors. The AHP provides a flexible, easily understood way to
assist the decision-maker in formulating a problem in a logical and
rational manner. A review of the AHP and a description of its
application in the assessment of the riskiness of constructing the
Jamuna multipurpose bridge in Bangladesh are included
Predicament and Countermeasure Research about Big Data Trading Platform in China
- P He
- X Wang
Research on the Big Data Transaction Platform Combining with the Data Analysis Service
- M Song
Humanities S O. Legal Supervision of Big Data Transaction in China from the Perspective of Transaction Security
- M Zhang
Trade Model of Smart Grid Big Data Based on Vector Evaluated Genetic Algorithm
- W Wang
- L Jiang
- Z Wang