Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Meteorology. / Microfiche copy available in Archives and Science. / Bibliography : leaves 81-82. / M.S.

Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2003. / Thesis advisor(s): Wendell A. Nuss, David S. Brown. Includes bibliographical references (p. 65-66). Also available online.

[pt] Os parques eólicos devem periodicamente desligar suas turbinas para realizar as manutenções agendadas. Uma vez que esta interrupção afeta a geração de energia e qualquer déficit na produção deve ser coberto por compras de energia no mercado spot, determinar o tempo ótimo para iniciar o trabalho de manutenção em um parque eólico é fundamental para maximizar sua receita, considerando que é função tanto da velocidade do vento esperada como dos preços spot da eletricidade. Neste trabalho, desenvolvemos um modelo para determinar o momento ideal para manutenção em um parque eólico. Analisamos uma janela de oportunidade no período mais provável do ano e realizamos atualizações semanais das velocidades esperadas do vento e previsões de preços de energia. As velocidades do vento são previstas com um modelo ARIMA enquanto os preços spot são simulados sob o modelo de programação estocástica dupla Newave. A decisão de adiar a manutenção para uma data futura é modelada como uma opção real americana. Testamos dois modelos com dados reais de um parque eólico no Nordeste brasileiro e comparamos nossos resultados com a prática atual e com o agendamento de manutenção considerando informações perfeitas para determinar os benefícios do modelo. Os resultados sugerem que esses modelos podem oferecer vantagens significativas em relação a uma decisão de parada que escolhe aleatoriamente uma semana para começar a manutenção dentro da janela de oportunidade e está perto da data de parada ideal, considerando o modelo de informação perfeita. / [en] Wind farms must periodically take their turbines offline in order to perform scheduled maintenance repairs. Since this interruption impacts the generation of energy and any shortfall in production must be covered by energy purchases in the spot market, determining the optimal time to start maintenance work at a wind farm is key to maximizing your revenue, which is a function of both the expected wind speeds and electricity spot prices. In this study we develop a model to determine the optimal maintenance schedule in a wind farm. We analyze a window of opportunity in the most likely period of the year and perform weekly updates of expected wind speeds and energy price forecasts. Wind speeds are forecasted with an ARIMA model, while spot prices are simulated under the Newave dual stochastic programing model. The decision to defer maintenance to a future date is modeled as an American real option. We test two models with actual data from a wind farm in the Brazilian Northeast, and compare our results with current practice and with maintenance scheduling considering perfect information in order to determine the benefits of the model. The results suggest that the models may provide significant advantages over a stopping decision that randomly chooses a week to begin maintenance within the opportunity window and is close to the ideal optimal stopping date considering perfect model.

There is a need for a fuel-optimal required time of arrival (RTA) mode for aircraft flight management systems capable of enabling controlled time of arrival functionality in the presence of wind speed forecast uncertainty. A computationally tractable two-stage stochastic algorithm utilizing a data-driven, location-specific forecast uncertainty model to generate forecast uncertainty scenarios is proposed as a solution. Three years of Aircraft Communications Addressing and Reporting Systems (ACARS) wind speed reports are used in conjunction with corresponding wind speed forecasts from the Rapid Update Cycle (RUC) forecast product to construct an inhomogeneous Markov model quantifying forecast uncertainty characteristics along specific route through the national airspace system. The forecast uncertainty modeling methodology addresses previously unanswered questions regarding the regional uncertainty characteristics of the RUC model, and realizations of the model demonstrate a clear tendency of the RUC product to be positively biased along routes following the normal contours of the jet stream. A two-stage stochastic algorithm is then developed to calculate the fuel optimal stage one cruise speed given a required time of arrival at a destination waypoint and wind forecast uncertainty scenarios generated using the inhomogeneous Markov model. The algorithm utilizes a quadratic approximation of aircraft fuel flow rate as a function of cruising Mach number to quickly search for the fuel-minimum stage one cruise speed while keeping computational footprint small and ensuring RTA adherence. Compared to standard approaches to the problem utilizing large scale linear programming approximations, the algorithm performs significantly better from a computational complexity standpoint, providing solutions in fractional power time while maintaining computational tractability in on-board systems.

MSc (Statistics) / Department of Statistics / Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance. / NRF