a b s t r a c tIn this paper the application of the Particle Swarm Optimization (PSO) method to estimate the Weibull parameters for wind resources in the Brazilian Northeast Region (BRNER) is reported. For the present research, wind speed data from three 80 m towers installed at different sites in the region were collected. The measuring periods for each tower site are: Petrolina. Aiming to compare with the PSO performance, five numerical methods are applied to calculate the Weibull distribution parameters. Best performance for all analyzed sites is achieved by the PSO method, with a correlation higher than 99% and an error close to zero. PSO proves to be a valuable technique for characterization of the particular wind conditions found in the BRNER.

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Power generation from decentralised renewable energy (RE) sources has been increasingly used worldwide. The authors apply portfolio theory (PT) to solar and wind resource forecast, combining those two intermittent RE sources in different percentages to investigate the resultant effects on the prediction error with the use of a proposed impact factor. They use solar and wind data from a weather station in Brazil's Northeast region. The use of PT to improve resource forecast of the specific solar and wind conditions found in that Brazilian region is a pioneer project and an original contribution of their research. Traditionally, PT has been used in the finance sector to reduce investment risks by diversifying applications. Considering predictability, the efficient frontier indicates an optimum portfolio for the period under investigation composed by 30% solar and 70% wind resource, obtained by the smallest calculated standard deviation. The obtained average forecast error for wind speed was −1.54% and for solar irradiance was 3.16%; the average forecast error resulting from the integration of 30% solar and 70% wind was −0.13%. This study innovates by using PT to solar and wind forecast in the planning phase, before the installation of wind and solar plants.

Photovoltaic (PV) power intermittence impacts electrical grid security and operation. Precise PV power and solar irradiation forecasts have been investigated as significant reducers of such impacts. Predicting solar irradiation involves uncertainties related to the characteristics of time series and their high volatility due to the dependence on many weather conditions. We propose a systematic review of PV power and solar resource forecasting, considering technical aspects related to each applied methodology. Our review covers the performance analysis of various physical, statistical, and machine learning models. These methodologies should contribute to decision-making, being applicable to different sites and climatic conditions. About 42% of the analyzed articles developed hybrid approaches, 83% performed short-term prediction, and more than 78% had, as forecast goal, PV power, solar irradiance, and solar irradiation. Considering spatial forecast scale, 66% predicted in a single field. As a trend for the coming years, we highlight the use of hybridized methodologies, especially those that optimize input and method parameters without loss of precision and postprocessing methodologies aiming at improvements in individualized applications.

Abstract:The existence of long and reliable streamflow data records is essential to establishing strategies for the operation of water resources systems. In areas where streamflow data records are limited or present missing values, rainfall-runoff models are typically used for reconstruction and/or extension of river flow series. The main objective of this paper is to verify the application of Kohonen Neural Networks (KNN) for estimating streamflows in Piancó River. The Piancó River basin is located in the Brazilian semiarid region, an area devoid of hydrometeorological data and characterized by recurrent periods of water scarcity. The KNN are unsupervised neural networks that cluster data into groups according to their similarities. Such models are able to classify data vectors even when there are missing values in some of its components, a very common situation in rainfall-runoff modeling. Twenty two years of rainfall and streamflow monthly data were used in order to calibrate and test the proposed model. Statistical indexes were chose as criteria for evaluating the performance of the KNN model under four different scenarios of input data. The results show that the proposed model was able to provide reliable estimations even when there were missing values in the input data set.