The Global Energy Assessment (GEA) brings together over 300 international researchers to provide an independent, scientifically based, integrated and policy-relevant analysis of current and emerging energy issues and options. It has been peer-reviewed anonymously by an additional 200 international experts. The GEA assesses the major global challenges for sustainable development and their linkages to energy; the technologies and resources available for providing energy services; future energy systems that address the major challenges; and the policies and other measures that are needed to realize transformational change toward sustainable energy futures. The GEA goes beyond existing studies on energy issues by presenting a comprehensive and integrated analysis of energy chalenges, opportunities and strategies, for developing, industrialized and emerging economies. This volume is a invaluable resource for energy specialists and technologists in all sectors (academia, industry and government) as well as policymakers, development economists and practitioners in international organizations and national governments.

Global Energy Assessment: Toward a Sustainable Future

In recent years, environmental considerations have prompted the use of wind power as a renewable energy resource. However, the biggest challenge in integrating wind power into the electric grid is its intermittency. One approach to deal with wind intermittency is forecasting future values of wind power production. Thus, several wind power or wind speed forecasting methods have been reported in the literature over the past few years. This paper provides insight on the foremost forecasting techniques, associated with wind power and speed, based on numeric weather prediction (NWP), statistical approaches, artificial neural network (ANN) and hybrid techniques over different time-scales. An overview of comparative analysis of various available forecasting techniques is discussed as well. In addition, this paper further gives emphasis on the major challenges and problems associated with wind power prediction.

A review of wind power and wind speed forecasting methods with different time horizons

On comparing three artificial neural networks for wind speed forecasting

This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly the same in order to compare two models (this fact makes it almost impossible to carry out a quantitative comparison between a huge number of models and methods). In place of a quantitative description, a qualitative approach is preferred for this review, remarking the contribution (and innovative aspect) of each model. On the basis of the review, some topics for future research are pointed out.

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A review on the young history of the wind power short-term prediction

Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems.

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Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine

Comparative study of the behavior of wind-turbines in a wind farm

Mathematical/statistical models based on purely autoregressive, fuzzy autoregressive and neural networks are being tested for wind power forecasting. Also, physical/meteorological models based on geostrophic wind estimates are being conjugated with a proposed sectorial discretization of the problem, in which the microscale orographic corrections and stability (non-neutral regime) corrections are applied only for those sectors where these corrections are capable of decreasing the prediction error. The time-scales (time-steps and horizons) have been defined and the parameter estimation and architecture optimisation methods have been developed. The Project focuses on short-range prediction oriented to the sales in the spot market, power system management and scheduling some maintenance tasks in the wind farms. The aim is to develop a real time computational tool in which the mathematical and physical forecasts are related in order to track always the ‘best’ estimate. In this way, advanced statistical tools are being employed to find the ‘best’ functions relating both forecasts, thus minimising the error of the final model. Results on a wind farm (from the company Acciona Energia) at the Northeast region of Spain are presented (32 machines of 660 kW). Hourly data of one year was selected, considering wind speed and direction from a reference mast and power output from all wind turbines. Apart from these measured data, reanalysis data from the ERA40 Project (ECMWF) are employed to the validation of the physical models. The models are tuned to use the meteorological forecast data in an optimal way.

Modelling the Integration of Mathematical and Physical Models for Short-term Wind Power Forecasting

Wind power forecasting remains a challenging area of research due to the complex relationships present in the conversion of wind into electrical power and the uncertainty associated with the physical modeling of the wind, which is highly dependent on the initial conditions. The need for a precise forecast is essential to meet the goals of high integration of renewable sources in the energy matrix to optimize non-renewable resources and improve the reliability in the operation of the electric system. In this work, we applied a wind power forecasting model based on Transformers that uses a self-attention encoder and a seq-2-seq decoder with global attention to capturing the nonlinear relationships of the power time series of wind farms. This evaluation considers the behavior of groups of wind farms with similar behavior. In terms of the similarity metric, five distant and close wind farms are clustered by the k-means method, using the autocorrelation features for time lags from 3 to 24 hours, mean and standard deviation, selected among 447 wind farms located in the NE region of Brazil. Compared to traditional linear autoregressive and LSTM models, the results show a lower prediction error. In this way, the model can capture temporal correlations arising from autoregressive and spatial behaviors. This shows the interdependence between hundreds of wind farms spread over a vast region with high wind potential, different altitudes, orography, distance from the coast, and wind regimes.

Alexandre Costa

Papers

A review on the young history of the wind power short-term prediction

Comparative study of the behavior of wind-turbines in a wind farm

Modelling the Integration of Mathematical and Physical Models for Short-term Wind Power Forecasting

Wind Power Forecast Based on Transformers and Clustering of Wind Farms with Temporal and Spatial Interdependence