Forecast study of the supply curve of solar and wind technologies in Argentina, Brazil, Chile and Mexico

Garcia-Heller, Veronica; Espinasa, Ramón; Paredes, Stephan

doi:10.1016/j.renene.2016.02.065

Cited by 20 publications

(6 citation statements)

References 7 publications

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“…In fact, Garcia-Heller et al (2016) observe in their study that large capacity is found in Argentina, followed by Brazil, Mexico and Chile. Specifically, the study estimates the wind power supply curve in such nations and creates a scenario for the year 2025, where the Brazilian potential is 26 GWh / year, considering the installation of three-bladed turbines.…”

Section: Specificities Of Wind Generation and Economic Viabilitymentioning

confidence: 90%

Análise de viabilidade econômico-financeira de um projeto eólico com simulação Monte Carlo e avaliação de risco

2017

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Resumo: O mundo está vivenciando uma profunda mudança climática, causada pela atividade predatória do homem, tendo em vista que as atividades econômicas foram exercidas sem preocupação com os impactos Palavras-chave: Energia eólica; Viabilidade econômico-financeira; Taxa Interna de Retorno. Abstract:The world is experiencing a deep climate change caused by the predatory activity of man, due to the long time economic activities practiced without concerns on the environmental impacts. However, nowadays there is the necessity to expand the current economy without compromising the necessary resources for the survival of the future generations. The intensive use of alternative energy configures a contribution to this new development path. In another perspective, the main technical analysis of economic and financial feasibility is widely known; however, the application of techniques that consider the risk is not so usual. Thus, this current work addresses whether it is possible to identify the economic feasibility of a potential wind power generation plant in different Brazilian locations considering the risk of applying Monte Carlo simulation and Beta distribution techniques. In order to answer this question tests were performed in four different locations in Brazil, utilizing the Internal Rate of Return (IRR) method with the mentioned techniques to consider the risk in investment analysis. The results showed the sensitivity of the wind project to the financing costs, regardless the region studied.

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Section: Specificities Of Wind Generation and Economic Viabilitymentioning

confidence: 90%

Análise de viabilidade econômico-financeira de um projeto eólico com simulação Monte Carlo e avaliação de risco

2017

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“…The cumulative installed capacity prediction of offshore wind farms is a complex nonlinear solution problem. So far, many scholars have proposed various prediction models for wind energy installation predictions in various countries 32‐44 . Vahidzadeh and Markfort 32 predicted the turbine power output curve by using a high‐resolution wind measurement power curve, including turbulence, yaw error, air density, wind direction, and shear; Huan et al 33 proposed a combined prediction model based on Set Empirical Mode Decomposition (EEMD) and Least‐Squares Support Vector Machine (LSSVM) to improve the accuracy and effectiveness of dissolved oxygen (DO) prediction; Kim and Hur 34 proposed a random prediction of wind power resources for the Jeju Island wind farm in South Korea using the enhanced set model.…”

Section: Literature Reviewmentioning

confidence: 99%

Future prospects research on offshore wind power scale in China based on signal decomposition and extreme learning machine optimized by principal component analysis

Liu

et al. 2020

Energy Science & Engineering

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“…As can be seen in Giebel et al, 36 Monteiro et al 37 for wind energy forecasts beyond 6 h ahead, it is known that purely statistical prediction models do not have performances as good as NWP models, since the latter represent better atmospheric changes. [36][37][38][39] As can be seen, given the conditions of the current problem, it is necessary to involve an analysis that will help (1) to discern in an objective (standardized) manner if the time series will be in the high or low power regime and (2) to increase the effectiveness of the NAR or sARIMA models when they are implemented.…”

Section: Selection Of Fmmentioning

confidence: 99%

“…Renewable energies have become a common alternative to generate electric power and have been established an important subject of governments' development schemes. In Garcia-Heller et al 1 government development schemes of Latin American Countries were analyzed. In all of those schemes, scenarios for year 2025, in which renewable energies should cover from 15% to 25% of total electrical demand, are presented.…”

Section: Introductionmentioning

confidence: 99%

Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

García

Calderón

González-A

et al. 2019

Advances in Mechanical Engineering

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Forecast models for wind speed and wind turbine power generation are valuable support tools for operators of Control Energy Center. In this work, a year of daily energy output of a wind turbine is analyzed. The original time series was separated into a high-power sample and a low-power sample. High-power sample has a seasonal pattern while lowpower sample does not. Afterward, a sARIMA model was produced for high-power sample forecast, with a good performance, while for low-power sample any ARIMA model defeated persistence model; thus, a couple of nonlinear autoregressive artificial neural networks are proposed. Mean absolute error and mean square error are reported and demonstrate that the sARIMA model can predict satisfactorily high-power sample, even with limited data, while to forecast low-power sample, it is necessary to use a neural networks approach and all data available to produce accurate forecasts. In each case, a normalized comparison with persistence model is also reported. Finally, a method which uses previous data of daily output energy and forecasted future wind speed values from a numeric weather prediction model is presented to objectively identify whether the current time is in a high-power or low-power regime to choose the ad hoc daily output energy forecast model.

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Forecast study of the supply curve of solar and wind technologies in Argentina, Brazil, Chile and Mexico

Cited by 20 publications

References 7 publications

Análise de viabilidade econômico-financeira de um projeto eólico com simulação Monte Carlo e avaliação de risco

Análise de viabilidade econômico-financeira de um projeto eólico com simulação Monte Carlo e avaliação de risco

Future prospects research on offshore wind power scale in China based on signal decomposition and extreme learning machine optimized by principal component analysis

Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

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