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Record W2802500217

The Feasibility of Using Wind Energy in Ilam

2016· article· en· W2802500217 on OpenAlexaboutno aff
Bakhtiyar Mohammadi, Homa Mahmoodi

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceWind powerEnergy (signal processing)MeteorologyComputer scienceStatisticsMathematicsEngineeringGeographyElectrical engineering
DOInot available

Abstract

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Renewable energy technologies convert renewable resources into forms of energy that can complement or replace conventional energy sources such as fossil fuels. Wind, solar, earth energy systems, small-scale hydro systems and biomass (eg. Straw, wood, corn) are all forms of renewable energy. Wind, solar and small-scale hydro systems have zero Greenhouse Gas Emissions. For example, for every kilowatt-hour generated by a wind turbine instead of by burning fossil fuels, about one kilogram of co2 is not emitted into the atmosphere (Alberta Environmentally Sustainable Agriculture Council, 2001). Wind turbines capture wind energy and convert it to electricity. Wind energy systems can either be small, stand-alone “off-grid” systems, or connected to the provincial power grid. Because wind is an intermittent resource, a back-up system is needed. Wind systems require an average annual wind speed greater then 15 kilometers per hour may only be feasible in some part Earth. Electricity generating coasts are reported to have dropped from $0.25 per kilowatt-hour (kWh) in the 1980’s to below $0.10 per kWh in 2001. One opportunity for farmers is the potential to lease land to wind energy producers. Every ten days, the earth receives solar energy of an amount equal to the world’s entire fossil fuel reserves, and approximately one precent of this is converted to wind energy (Freris, 1990). This solar radiation is converted to wind energy as a result of the unequal heating of the equator as compared to the poles, and of the oceans as compared to the continents. This unequal heating leads to motion within the atmosphere as it tries to equalize its pressure- resulting in what we know as wind. A second cause wind is the motion of the earth. Many meteorological quantities are transported via air currents. In fact, because of the winds role in the transmission of physical and meteorological parameters atmospheric, are very important. Further movement of wind as a source of new and inexhaustible energy is considered. In recent years, the kinetic energy of wind as a source of new and inexhaustible energy is considered by many countries. Recently use of wind energy as one of the most popular renewable power resources for producing electrical energy, is growing up. The purpose of this study is evaluating the amount of wind energy production and finding the windward areas in the Ilam province. In this study, the wind speed and wind direction daily data from 7 weather stations in the state (from established year to 2013) and 15 weather stations from outside of the state boundary are collected. At First, the days with incomplete data are eliminated, then for unifying dimensions of each data base, the average of long-term daily data are calculated. For averaging the long-term daily data related to wind speed and wind direction, two data bases with the 366*22 dimensions are established separately. By utilizing the two data bases, the orbital and meridional components are calculated. Based on the orbital and meridional components, and using krigingchr('39')s geostatistics method, the orbital and meridional components of wind speed of the area study, are estimated. Finally, tow data bases with the 896*366 dimensions are created for the Ilam province that 366 of data are belonged to the orbital and meridional components of wind speed average and 896 of data are belonged to estimated cells in the Ilam province. The dimensions of each cell were 4.7*4.7 square kilometers. With cluster analysis of the two data bases, the windward areas of Ilam province are specified. For better understanding of wind speed characteristics, the monthly maps of windward areas of Ilam province and the annual coefficient of variability of wind speed are plotted. Based on wind speed, wind density, and the size of utilized wind turbine (rotatory radius 5, 10, 15, 20 meters), the amount of wind power generations (from 896 cells), are estimated. The annually and monthly equipotential maps of wind power generation are plotted. The results show that Mehran is the most windward area in the Ilam province. Also the western areas of Ilam province have more wind speed availability compared to the eastern areas of the Ilam province. Darehshahr has minimum average wind speed in all months of the year. The variability coefficient of wind speed in the Ilam province is between 17.2 and 40.6. The northern areas of the state have less variability coefficient compared to other areas of the state. The evaluation findings of the four mentioned wind turbines show that the wind turbines can produce maximum wind power generation at the west areas of the state (Mehran). Among all of the months in a year, the July has the most wind power generation, as the time viewpoints. By utilizing the wind turbines with five meters blades, the amount of wind production threshold is about 1 to 11 million watts per squared meters. Also by utilizing the wind turbines with 10 and 15 meters blades, the amount of minimum and maximum annual wind energy generation can be about 5 to 45, and 12 to 101, million watts per squared meters, respectively.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0380.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.271
GPT teacher head0.514
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2016
Admission routes1
Has abstractyes

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