Wind Energy Resource Atlas of Oaxaca

Elliott, D.; Schwartz, M.; Scott, G.; Haymes, S.; Heimiller, D.; George, R.

doi:10.2172/15004364

Cited by 59 publications

(33 citation statements)

References 2 publications

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“…La Ventosa county is characterized by a wind power density above 800 Watts per squared meter (800 W/m 2 ), and a mean wind speed (at 50 m height) greater than 8.5 m per second (50 m/s) [43]. Furthermore, it has been found that La Ventosa wind speed distribution is explained better by a bimodal probability distribution than the Weibull distribution [6,7].…”

Section: Data Descriptionmentioning

confidence: 99%

Wind speed forecasting for wind farms: A method based on support vector regression

2016

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a b s t r a c tIn this paper, a hybrid methodology based on Support Vector Regression for wind speed forecasting is proposed. Using the autoregressive model called Time Delay Coordinates, feature selection is performed by the Phase Space Reconstruction procedure. Then, a Support Vector Regression model is trained using univariate wind speed time series. Parameters of Support Vector Regression are tuned by a genetic algorithm. The proposed method is compared against the persistence model, and autoregressive models (AR, ARMA, and ARIMA) tuned by Akaike's Information Criterion and Ordinary Least Squares method. The stationary transformation of time series is also evaluated for the proposed method. Using historical wind speed data from the Mexican Wind Energy Technology Center (CERTE) located at La Ventosa, Oaxaca, M exico, the accuracy of the proposed forecasting method is evaluated for a whole range of short termforecasting horizons (from 1 to 24 h ahead). Results show that, forecasts made with our method are more accurate for medium (5e23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models.

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Section: Data Descriptionmentioning

confidence: 99%

Wind speed forecasting for wind farms: A method based on support vector regression

2016

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“…It has 570 municipalities, 418 of which are governed by an indigenous form of governance -usos y costumbres 4 -based on consensus decision-making in communal assemblies (Stephen 2002;Altamirano-Jiménez 2014 (Elliott et al, 2003), the region has excellent wind resources and is now considered 'home to some of the best wind resources on earth' (IFC 2014, 1). By January 2015, the region had 1608 wind turbines (Rivas 2015).…”

Section: Background: Oaxaca Istmo and The Bíi Hioxo Wind Parkmentioning

confidence: 99%

Counterinsurgency for wind energy: the Bíi Hioxo wind park in Juchitán, Mexico

Dunlap

2017

The Journal of Peasant Studies

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Sustainable development and climate change mitigation policies, Dunlap and Fairhead argue, have instigated and renewed old conflicts over land and natural resources, deploying military techniques of counterinsurgency to achieve land control. Wind energy development, a popular tool of climate change mitigation policies, has consequently generated conflict in the Isthmus of Tehuantepec (Istmo) region in Oaxaca, Mexico. Research is based on participant observation and 20 recorded interviews investigating the Fuerza y Energía Bíi Hioxo Wind Farm on the outskirts of Juchitán de Zaragoza. This paper details the repressive techniques employed by state, private and informal authorities against popular opposition to the construction of the Bíi Hioxo wind park on communal land. Providing background on Juchitán, social property and counterinsurgency in Southern Mexico, this paper analyzes the development of the Bíi Hioxo wind park. It further explores the emergence of 'hard' and 'soft' counterinsurgency techniques used to pacify resistance against the wind park, enabling its completion next to the Lagoon Superior in October 2014. Discussing the 'greening of counterinsurgency', this contribution concludes that the Bíi Hioxo wind park has spawned social divisions and violent conflict, and intervened in the sensitive cultural fabric of Istmeño life.

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“…The data provided an estimate of the annual average wind resource for the conterminous United States, with a resolution of 1/3 degree of latitude by 1/4 degree of longitude. 34 At the time, the 1986 U.S. wind resource map provided the information required to enable prospecting for potential wind power plant locations in areas other than the already well-known locations in California.…”

Section: Land-based Windmentioning

confidence: 99%

Wind and solar resource data sets

Clifton

Draxl

Badger

et al. 2017

WIREs Energy & Environment

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The range of resource data sets spans from static cartography showing the mean annual wind speed or solar irradiance across a region to high temporal and high spatial resolution products that provide detailed information at a potential wind or solar energy facility. These data sets are used to support continental‐scale, national, or regional renewable energy development; facilitate prospecting by developers; and enable grid integration studies. This review first provides an introduction to the wind and solar resource data sets, then provides an overview of the common methods used for their creation and validation. A brief history of wind and solar resource data sets is then presented, followed by areas for future research. WIREs Energy Environ 2018, 7:e276. doi: 10.1002/wene.276 This article is categorized under: Wind Power > Climate and Environment Solar Heating and Cooling > Climate and Environment Energy Infrastructure > Climate and Environment

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Wind Energy Resource Atlas of Oaxaca

Cited by 59 publications

References 2 publications

Wind speed forecasting for wind farms: A method based on support vector regression

Wind speed forecasting for wind farms: A method based on support vector regression

Counterinsurgency for wind energy: the Bíi Hioxo wind park in Juchitán, Mexico

Wind and solar resource data sets

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