Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,038 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».