Clustering of Complementary Electricity Consumers Based on Their Usage Patterns

In the electricity market, the real-time balance of electricity generation and consumption is a main task. In view of this, power providers usually sign contracts with their critical consumers (i.e., usually large-scale industrial companies) for managing their capacity demands. On the other hand, aggregators group commercial and residential consumers, and integrate their demands to negotiate with power providers. With a proper grouping of numerous electricity consumers, aggregators help to ensure stable electric supply, and reduce the burden of managing many consumers. In this work, we thus propose a novel data clustering approach to group complementary consumers based on their usage patterns (i.e., daily electricity consumption curves.) Furthermore, we incorporate the technique of discrete wavelet transform to speed up the clustering process. Specifically, approximations reconstructed from only a few wavelet coefficients may precisely capture the shape of original usage patterns. Experimental results based on a real dataset show that our approach is promising in practical applications.

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Zero Emission Building And Conversion Factors Between Electricity Consumption And Emissions Of Greenhouse Gases In A Long Term Perspective

Environmental and Climate Technologies ◽

10.2478/rtuect-2014-0002 ◽

2014 ◽

Vol 13 (1) ◽

pp. 12-19 ◽

Cited By ~ 23

Author(s):

Ingeborg Graabak ◽

Bjørn Harald Bakken ◽

Nicolai Feilberg

Keyword(s):

Power System ◽

Electricity Market ◽

Large Scale ◽

Conversion Factor ◽

Electricity Consumption ◽

Life Time ◽

Market Model ◽

Conversion Factors ◽

Zero Emission

Abstract The CO2 emissions from a building’s power system will change over the life time of the building, and this need to be taken into account to verify whether a building is Zero Emission (ZEB) or not. This paper describes how conversion factors between electricity demand and emissions can be calculated for the European power system in a long term perspective through the application of a large scale electricity market model (EMPS). Examples of two types of factors are given: a conversion factor for average emissions per kWh for the whole European power system as well as a marginal factor for a specific region.

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Multilevel Clustering of Induction Rules

International Journal of Systems and Service-Oriented Engineering ◽

10.4018/ijssoe.2014070101 ◽

2014 ◽

Vol 4 (3) ◽

pp. 1-25

Author(s):

Amine Chemchem ◽

Habiba Drias ◽

Youcef Djenouri

Keyword(s):

Data Mining ◽

Large Scale ◽

Similarity Measures ◽

Intelligent Agent ◽

Cognitive Agent ◽

Data Mining Techniques ◽

The Core ◽

Recent Emergence ◽

Speed Up ◽

Clustering Approach

The tremendous size of data in nowadays world web invokes many data mining techniques. The recent emergence of some new data mining techniques provide also many interesting induction rules. So, it's important to process these induction rules in order to extract some new strong patterns called meta-rules. This work explores this concept by proposing a new support for induction rules clustering. Besides, a new clustering approach based on multilevel paradigm called multilevel clustering is developed for the purpose of treating large scale knowledge sets. The approach invokes k-means algorithm to cluster induction rules using new designed similarity measures. The developed module have been implemented in the core of the cognitive agent, in order to speed up its reasoning. This new architecture called Multilevel Miner Intelligent Agent (MMIA) is tested on four public benchmarks that contain 25000 rules, and compared to the classical one. As foreseeable, the multilevel clustering outperforms clearly the basic k-means algorithm on both the execution time and success rate criteria.

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Speed-up of simulation of magnetization process for large-scale HTS undulator of X-FEL based on power-law macro-model

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-209413 ◽

2020 ◽

Vol 64 (1-4) ◽

pp. 985-991

Author(s):

Yi Deri ◽

Hideki Kawaguchi

Keyword(s):

Power Law ◽

Large Scale ◽

Multipole Expansion ◽

Electron Trajectory ◽

Magnetization Process ◽

Simulation Code ◽

Practical Applications ◽

Macro Model ◽

Speed Up ◽

Intensity Magnetic Field

The Pure-type HTS undulator is proposed to achieve a high-intensity magnetic field and small size undulator for a compact free-electron laser (FEL). A high precision simulation is required before making the real machine since the sizes and positions are difficult to adjust after the HTSs are magnetized in the cryostat. For this purpose, authors have been developed a numerical simulation code for the magnetization process of HTS undulator of X-FEL based on the power-law macro-model. In this paper, the previously developed simulation code can be speeded up by carefully optimizing the parameters of the power-law macro-model for the 3-HTS magnets model and a large-scale simulation can be performed in an acceptable time by using a multipole expansion for the Biot–Savart law. In addition, for practical applications, the influence of the fluctuation of the magnets thickness and critical current for the electron trajectory are evaluated by using the speed-up simulation code.

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Feature Fusion: An Application To Biomedical Signal Classification

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f7108.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 5844-5849

Keyword(s):

Correlation Analysis ◽

Large Scale ◽

Feature Fusion ◽

Discrete Wavelet ◽

Volume Data ◽

Biomedical Signal ◽

Speed Up ◽

Independent Features ◽

Electroencephalogram Eeg

Development of a feasible support system for automating staging of neural disorder based on Electroencephalogram (EEG) is essential to speed-up diagnosis process by improving the burden of the clinician of analyzing large volume data and to accelerate large scale research. In this work Discrete wavelet transform (DWT) has been applied to extract statistically independent features and fused the features for effective classification of various EEG signal. The aim of this paper is to present a comparative study of two feature fusion approaches namely Canonical Correlation Analysis (CCA) and Discriminant Correlation Analysis (DCA). Further, our proposed method can be extended to develop a graphical user interface and promote real time implementation.

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Bullying, School Violence, and Climate in Evolving Contexts

10.1093/oso/9780190663049.001.0001 ◽

2018 ◽

Cited By ~ 4

Author(s):

Ron Avi Astor ◽

Rami Benbenisthty

Keyword(s):

School Violence ◽

School Safety ◽

Media Coverage ◽

Large Scale ◽

Ecological Model ◽

Empirical Studies ◽

Practical Applications ◽

Teacher Student ◽

Area Of Interest ◽

New Research

Since 2005, the bullying, school violence, and school safety literatures have expanded dramatically in content, disciplines, and empirical studies. However, with this massive expansion of research, there is also a surprising lack of theoretical and empirical direction to guide efforts on how to advance our basic science and practical applications of this growing scientific area of interest. Parallel to this surge in interest, cultural norms, media coverage, and policies to address school safety and bullying have evolved at a remarkably quick pace over the past 13 years. For example, behaviors and populations that just a decade ago were not included in the school violence, bullying, and school safety discourse are now accepted areas of inquiry. These include, for instance, cyberbullying, sexting, social media shaming, teacher–student and student–teacher bullying, sexual harassment and assault, homicide, and suicide. Populations in schools not previously explored, such as lesbian, gay, bisexual, transgender, and queer students and educators and military- and veteran-connected students, become the foci of new research, policies, and programs. As a result, all US states and most industrialized countries now have a complex quilt of new school safety and bullying legislation and policies. Large-scale research and intervention funding programs are often linked to these policies. This book suggests an empirically driven unifying model that brings together these previously distinct literatures. This book presents an ecological model of school violence, bullying, and safety in evolving contexts that integrates all we have learned in the 13 years, and suggests ways to move forward.

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Optimal Sizing and Placement of Capacitor Banks in Distribution Networks Using a Genetic Algorithm

Electricity ◽

10.3390/electricity2020012 ◽

2021 ◽

Vol 2 (2) ◽

pp. 187-204

Author(s):

Gian Giuseppe Soma

Keyword(s):

Genetic Algorithm ◽

Electricity Market ◽

Distribution Systems ◽

Reactive Power ◽

Electricity Consumption ◽

Distribution Networks ◽

Optimal Placement ◽

Capacitor Banks ◽

Control Pattern ◽

Definition Of

Nowadays, response to electricity consumption growth is mainly supported by efficiency; therefore, this is the new main goal in the development of electric distribution networks, which must fully comply with the system’s constraints. In recent decades, the issue of independent reactive power services, including the optimal placement of capacitors in the grid due to the restructuring of the electricity industry and the creation of a competitive electricity market, has received attention from related companies. In this context, a genetic algorithm is proposed for optimal planning of capacitor banks. A case study derived from a real network, considering the application of suitable daily profiles for loads and generators, to obtain a better representation of the electrical conditions, is discussed in the present paper. The results confirmed that some placement solutions can be obtained with a good compromise between costs and benefits; the adopted benefits are energy losses and power factor infringements, taking into account the network technical limits. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of capacitor banks in distribution systems, with the definition of a suitable control pattern, have been proved.

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Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

Applied Sciences ◽

10.3390/app11104438 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4438

Author(s):

Satyendra Singh ◽

Manoj Fozdar ◽

Hasmat Malik ◽

Maria del Valle Fernández Moreno ◽

Fausto Pedro García Márquez

Keyword(s):

Wind Power ◽

Electricity Market ◽

Large Scale ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Optimization Methods ◽

Bidding Strategy ◽

Wind Speed Profile ◽

Gravitational Search ◽

Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

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2D Nanomaterials for Effective Energy Scavenging

Nano-Micro Letters ◽

10.1007/s40820-021-00603-9 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Md Al Mahadi Hasan ◽

Yuanhao Wang ◽

Chris R. Bowen ◽

Ya Yang

Keyword(s):

Large Scale ◽

Waste Heat ◽

Material Selection ◽

Power Supplies ◽

Small Scale ◽

Energy Scavenging ◽

Power Sources ◽

Practical Applications ◽

2D Nanomaterials ◽

Self Powered

AbstractThe development of a nation is deeply related to its energy consumption. 2D nanomaterials have become a spotlight for energy harvesting applications from the small-scale of low-power electronics to a large-scale for industry-level applications, such as self-powered sensor devices, environmental monitoring, and large-scale power generation. Scientists from around the world are working to utilize their engrossing properties to overcome the challenges in material selection and fabrication technologies for compact energy scavenging devices to replace batteries and traditional power sources. In this review, the variety of techniques for scavenging energies from sustainable sources such as solar, air, waste heat, and surrounding mechanical forces are discussed that exploit the fascinating properties of 2D nanomaterials. In addition, practical applications of these fabricated power generating devices and their performance as an alternative to conventional power supplies are discussed with the future pertinence to solve the energy problems in various fields and applications.

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Application of Novel Non-Thermal Physical Technologies to Degrade Mycotoxins

Journal of Fungi ◽

10.3390/jof7050395 ◽

2021 ◽

Vol 7 (5) ◽

pp. 395

Author(s):

Mohammad Yousefi ◽

Masoud Aman Mohammadi ◽

Maryam Zabihzadeh Khajavi ◽

Ali Ehsani ◽

Vladimír Scholtz

Keyword(s):

Electron Beam ◽

Food Systems ◽

Large Scale ◽

Plasma Electron ◽

Pulsed Light ◽

Beam Radiation ◽

Practical Applications ◽

Electron Beam Radiation ◽

Non Thermal Plasma ◽

Agriculture And Food

Mycotoxins cause adverse effects on human health. Therefore, it is of the utmost importance to confront them, particularly in agriculture and food systems. Non-thermal plasma, electron beam radiation, and pulsed light are possible novel non-thermal technologies offering promising results in degrading mycotoxins with potential for practical applications. In this paper, the available publications are reviewed—some of them report efficiency of more than 90%, sometimes almost 100%. The mechanisms of action, advantages, efficacy, limitations, and undesirable effects are reviewed and discussed. The first foretastes of plasma and electron beam application in the industry are in the developing stages, while pulsed light has not been employed in large-scale application yet.

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A Short Review on Recent Advances of Hydrogel-Based Adsorbents for Heavy Metal Ions

Metals ◽

10.3390/met11060864 ◽

2021 ◽

Vol 11 (6) ◽

pp. 864

Author(s):

Suguna Perumal ◽

Raji Atchudan ◽

Thomas Nesakumar Jebakumar Immanuel Edison ◽

Rajendran Suresh Babu ◽

Petchimuthu Karpagavinayagam ◽

...

Keyword(s):

Heavy Metal ◽

Metal Ions ◽

Heavy Metal Ions ◽

Large Scale ◽

Water Bodies ◽

Dimensional Structure ◽

High Porosity ◽

Adsorption Method ◽

Toxic Heavy Metal ◽

Practical Applications

The growth of industry fulfills our necessity and promotes economic development. However, pollutants from such industries pollute water bodies which pose a high risk for living organisms. Thus, researchers have been urged to develop an efficient method to remove toxic heavy metal ions from water bodies. The adsorption method shows promising results for the removal of heavy metal ions and is easy to operate on a large scale, thus can be applied to practical applications. Numerous adsorbents were developed and reported, among them hydrogels, which attract great attention because of the reusability, ease of preparation, and handling. Hydrogels are generally prepared by the cross-linking of polymers that result in a three-dimensional structure, showing high porosity and high functionality. They are hydrophilic in nature because of the functional groups, and are non-toxic. Thus, this review provides various methods of hydrogel adsorbents preparation and summarizes recent progress in the use of hydrogel adsorbents for the removal of heavy metal ions. Further, the mechanism involved in the removal of heavy metal ions is briefly discussed. The most recent studies about the adsorption method for the treatment of heavy metal ions contaminated water are presented.

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