Entwicklung eines Windleistungprognosemodells zur Verbesserung der Kraftwerkseinsatzplanung

Die Maßnahmen zur Förderung der Windenergie in Deutschland haben wichtige Anstöße zur technologischen Weiterentwicklung geliefert und die Grundlagen für den enormen Anlagenzubau geschaffen. Die installierte Windleistung hat heute eine beachtliche Größenordnung erreicht und ein weiteres Wachstum in ähnlichen Dimensionen ist auch für die nächsten Jahre zu erwarten. Die aus Wind erzeugte elektrische Leistung deckt bereits heute in einigen Netzbereichen die Netzlast zu Schwachlastzeiten. Dies zeigt, dass die Windenergie ein nicht mehr zu vernachlässigender Faktor in der elektrischen Energieversorgung geworden ist. Im Rahmen der Kraftwerkseinsatzplanung sind Betrag und Verlauf der Windleistung des folgenden Tages mittlerweile zu wichtigen und zugleich schwierig zu bestimmenden Variablen geworden. Starke Schwankungen und falsche Prognosen der Windstromeinspeisung verursachen zusätzlichen Bedarf an Regel- und Ausgleichsleistung durch die Systemführung. Das im Rahmen dieser Arbeit entwickelte Prognosemodell liefert die zu erwartenden Windleistungen an 16 repräsentativen Windparks bzw. Gruppen von Windparks für bis zu 48 Stunden im Voraus. Aufgrund von prognostizierten Wetterdaten des deutschen Wetterdienstes (DWD) werden die Leistungen der einzelnen Windparks mit Hilfe von künstlichen neuronalen Netzen (KNN) berechnet. Diese Methode hat gegenüber physikalischen Verfahren den Vorteil, dass der komplexe Zusammenhang zwischen Wettergeschehen und Windparkleistung nicht aufwendig analysiert und detailliert mathematisch beschrieben werden muss, sondern anhand von Daten aus der Vergangenheit von den KNN gelernt wird. Das Prognosemodell besteht aus zwei Modulen. Mit dem ersten wird, basierend auf den meteorologischen Vorhersagen des DWD, eine Prognose für den Folgetag erstellt. Das zweite Modul bezieht die online gemessenen Leistungsdaten der repräsentativen Windparks mit ein, um die ursprüngliche Folgetagsprognose zu verbessern und eine sehr genaue Kurzzeitprognose für die nächsten drei bis sechs Stunden zu berechnen. Mit den Ergebnissen der Prognosemodule für die repräsentativen Standorte wird dann über ein Transformationsmodell, dem so genannten Online-Modell, die Gesamteinspeisung in einem größeren Gebiet berechnet. Das Prognoseverfahren hat seine besonderen Vorzüge in der Genauigkeit, den geringen Rechenzeiten und den niedrigen Betriebskosten, da durch die Verwendung des bereits implementierten Online-Modells nur eine geringe Anzahl von Vorhersage- und Messstandorten benötigt wird. Das hier vorgestellte Prognosemodell wurde ursprünglich für die E.ON-Netz GmbH entwickelt und optimiert und ist dort seit Juli 2001 im Einsatz. Es lässt sich jedoch auch leicht an andere Gebiete anpassen. Benötigt werden dazu nur die Messdaten der Leistung ausgewählter repräsentativer Windparks sowie die dazu gehörenden Wettervorhersagen, um die KNN entsprechend zu trainieren.

A note on atmospheric predictability

Using the method of Lorenz (1982), we have estimated the predictability of a recent version of the European Center for Medium-Range Weather Forecasting (ECMWF) model using two different estimates of the initial error corresponding to 6- and 24-hr forecast errors, respectively. For a 6-hr forecast error of the extratropical 500-hPa geopotential height field, a potential increase in forecast skill by more than 3 d is suggested, indicating a further increase in predictability by another 1.5 d compared to the use of a 24-hr forecast error. This is due to a smaller initial error and to an initial error reduction resulting in a smaller averaged growth rate for the whole 7-d forecast. A similar assessment for the tropics using the wind vector fields at 850 and 250 hPa suggests a huge potential improvement with a 7-d forecast providing the same skill as a 1-d forecast now. A contributing factor to the increase in the estimate of predictability is the apparent slow increase of error during the early part of the forecast.

Evaluation of the S(T) assimilation method with the Argo dataset

Recent observations from the Argo dataset of temperature and salinity profiles are used to evaluate a series of 3-year data assimilation experiments in a global ice–ocean general circulation model. The experiments are designed to evaluate a new data assimilation system whereby salinity is assimilated along isotherms, S(T ). In addition, the role of a balancing salinity increment to maintain water mass properties is investigated. This balancing increment is found to effectively prevent spurious mixing in tropical regions induced by univariate temperature assimilation, allowing the correction of isotherm geometries without adversely influencing temperature–salinity relationships. In addition, the balancing increment is able to correct a fresh bias associated with a weak subtropical gyre in the North Atlantic using only temperature observations. The S(T ) assimilation method is found to provide an important improvement over conventional depth level assimilation, with lower root-mean-squared forecast errors over the upper 500 m in the tropical Atlantic and Pacific Oceans. An additional set of experiments is performed whereby Argo data are withheld and used for independent evaluation. The most significant improvements from Argo assimilation are found in less well-observed regions (Indian, South Atlantic and South Pacific Oceans). When Argo salinity data are assimilated in addition to temperature, improvements to modelled temperature fields are obtained due to corrections to model density gradients and the resulting circulation. It is found that observations from the Argo array provide an invaluable tool for both correcting modelled water mass properties through data assimilation and for evaluating the assimilation methods themselves.

An evaluation of the performance of UK real estate forecasters

Given the significance of forecasting in real estate investment decisions, this paper investigates forecast uncertainty and disagreement in real estate market forecasts. It compares the performance of real estate forecasters with non-real estate forecasters. Using the Investment Property Forum (IPF) quarterly survey amongst UK independent real estate forecasters and a similar survey of macro-economic and capital market forecasters, these forecasts are compared with actual performance to assess a number of forecasting issues in the UK over 1999-2004, including forecast error, bias and consensus. The results suggest that both groups are biased, less volatile compared to market returns and inefficient in that forecast errors tend to persist. The strongest finding is that forecasters display the characteristics associated with a consensus indicating herding.

Evaluating forecasts from SETAR models of exchange rates

We consider the forecasting performance of two SETAR exchange rate models proposed by Kräger and Kugler [J. Int. Money Fin. 12 (1993) 195]. Assuming that the models are good approximations to the data generating process, we show that whether the non-linearities inherent in the data can be exploited to forecast better than a random walk depends on both how forecast accuracy is assessed and on the ‘state of nature’. Evaluation based on traditional measures, such as (root) mean squared forecast errors, may mask the superiority of the non-linear models. Generalized impulse response functions are also calculated as a means of portraying the asymmetric response to shocks implied by such models.

Forecasting with difference and trend stationary models

Although difference-stationary (DS) and trend-stationary (TS) processes have been subject to considerable analysis, there are no direct comparisons for each being the data-generation process (DGP). We examine incorrect choice between these models for forecasting for both known and estimated parameters. Three sets of Monte Carlo simulations illustrate the analysis, to evaluate the biases in conventional standard errors when each model is mis-specified, compute the relative mean-square forecast errors of the two models for both DGPs, and investigate autocorrelated errors, so both models can better approximate the converse DGP. The outcomes are surprisingly different from established results.

Rationality and momentum in real estate investment forecasts

This study examines the rationality and momentum in forecasts for rental, capital value and total returns for the real estate investment market in the United Kingdom. In order to investigate if forecasters are affected by the general economic conditions present at the time of forecast we incorporate into the analysis Gross Domestic Product(GDP) and the Default Spread (DS). The empirical findings show high levels of momentum in the forecasts, with highly persistent forecast errors. The results also indicate that forecasters are affected by adverse conditions. This is consistent with the finding that they tend to exhibit greater forecast error when the property market is underperforming and vice-versa.

A route to systematic error in forecasts of Rossby waves

Recent work has shown that both the amplitude of upper-level Rossby waves and the tropopause sharpness decrease with forecast lead time for several days in some operational weather forecast systems. In this contribution, the evolution of error growth in a case study of this forecast error type is diagnosed through analysis of operational forecasts and hindcast simulations. Potential vorticity (PV) on the 320-K isentropic surface is used to diagnose Rossby waves. The Rossby-wave forecast error in the operational ECMWF high-resolution forecast is shown to be associated with errors in the forecast of a warm conveyor belt (WCB) through trajectory analysis and an error metric for WCB outflows. The WCB forecast error is characterised by an overestimation of WCB amplitude, a location of the WCB outflow regions that is too far to the southeast, and a resulting underestimation of the magnitude of the negative PV anomaly in the outflow. Essentially the same forecast error development also occurred in all members of the ECMWF Ensemble Prediction System and the Met Office MOGREPS-15 suggesting that in this case model error made an important contribution to the development of forecast error in addition to initial condition error. Exploiting this forecast error robustness, a comparison was performed between the realised flow evolution, proxied by a sequence of short-range simulations, and a contemporaneous forecast. Both the proxy to the realised flow and the contemporaneous forecast a were produced with the Met Office Unified Model enhanced with tracers of diabatic processes modifying potential temperature and PV. Clear differences were found in the way potential temperature and PV are modified in the WCB between proxy and forecast. These results demonstrate that differences in potential temperature and PV modification in the WCB can be responsible for forecast errors in Rossby waves.

The prediction of Northern Hemisphere tropical cyclone extended life cycles by the ECMWF Ensemble and Deterministic Prediction Systems. Part I: tropical cyclone stage

This study has explored the prediction errors of tropical cyclones (TCs) in the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) for the Northern Hemisphere summer period for five recent years. Results for the EPS are contrasted with those for the higher-resolution deterministic forecasts. Various metrics of location and intensity errors are considered and contrasted for verification based on IBTrACS and the numerical weather prediction (NWP) analysis (NWPa). Motivated by the aim of exploring extended TC life cycles, location and intensity measures are introduced based on lower-tropospheric vorticity, which is contrasted with traditional verification metrics. Results show that location errors are almost identical when verified against IBTrACS or the NWPa. However, intensity in the form of the mean sea level pressure (MSLP) minima and 10-m wind speed maxima is significantly underpredicted relative to IBTrACS. Using the NWPa for verification results in much better consistency between the different intensity error metrics and indicates that the lower-tropospheric vorticity provides a good indication of vortex strength, with error results showing similar relationships to those based on MSLP and 10-m wind speeds for the different forecast types. The interannual variation in forecast errors are discussed in relation to changes in the forecast and NWPa system and variations in forecast errors between different ocean basins are discussed in terms of the propagation characteristics of the TCs.

Exploiting existing ground-based remote sensing networks to improve high-resolution weather forecasts

A new generation of high-resolution (1 km) forecast models promises to revolutionize the prediction of hazardous weather such as windstorms, flash floods, and poor air quality. To realize this promise, a dense observing network, focusing on the lower few kilometers of the atmosphere, is required to verify these new forecast models with the ultimate goal of assimilating the data. At present there are insufficient systematic observations of the vertical profiles of water vapor, temperature, wind, and aerosols; a major constraint is the absence of funding to install new networks. A recent research program financed by the European Union, tasked with addressing this lack of observations, demonstrated that the assimilation of observations from an existing wind profiler network reduces forecast errors, provided that the individual instruments are strategically located and properly maintained. Additionally, it identified three further existing European networks of instruments that are currently underexploited, but with minimal expense they could deliver quality-controlled data to national weather services in near–real time, so the data could be assimilated into forecast models. Specifically, 1) several hundred automatic lidars and ceilometers can provide backscatter profiles associated with aerosol and cloud properties and structures with 30-m vertical resolution every minute; 2) more than 20 Doppler lidars, a fairly new technology, can measure vertical and horizontal winds in the lower atmosphere with a vertical resolution of 30 m every 5 min; and 3) about 30 microwave profilers can estimate profiles of temperature and humidity in the lower few kilometers every 10 min. Examples of potential benefits from these instruments are presented.

Previsões de inflação com a curva de Phillips

O objetivo do trabalho investigar qualidade das previsões da taxa de inflação brasileira utilizando-se uma alternativa tradicional unemployment rate Phillips curve. Utilizaremos diversas variáveis que espelham nível de atividade econômica no Brasil em substituição ao hiato entre taxa de desemprego taxa natural de desemprego (NAIRU). Essas variáveis serão trabalhadas e baseado em critérios mencionados ao longo do estudo, serão classificadas por nível de erro de previsibilidade. objetivo ao final do trabalho sugerir indicadores variáveis de nível de atividade disponíveis publicamente que melhor possam interagir com dinâmica da inflação brasileira.

Forecasting electricity load demand: analysis of the 2001 rationing period in Brazil

This paper studies the electricity load demand behavior during the 2001 rationing period, which was implemented because of the Brazilian energetic crisis. The hourly data refers to a utility situated in the southeast of the country. We use the model proposed by Soares and Souza (2003), making use of generalized long memory to model the seasonal behavior of the load. The rationing period is shown to have imposed a structural break in the series, decreasing the load at about 20%. Even so, the forecast accuracy is decreased only marginally, and the forecasts rapidly readapt to the new situation. The forecast errors from this model also permit verifying the public response to pieces of information released regarding the crisis.

Estudo comparativo do poder preditivo do PIB brasileiro por variáveis macroeconômicas em relação ao conjunto de variáveis da estrutura de taxa de juros

O trabalho busca comparar dois conjuntos de informações para a projeção das variações do PIB brasileiro: através de modelos econométricos aplicados sobre a série univariada do PIB, e a aplicação dos mesmos modelos, mas contemplando adicionalmente o conjunto de informação com dados da estrutura a termo de taxa de juros de swap PRÉ-DI. O objetivo é verificar, assim como descrito na literatura internacional, se informações de variáveis financeiras tem a capacidade de incrementar o poder preditivo de projeções de variáveis macroeconômicas, na medida em que esses dados também embutem as expectativas dos agentes em relação ao desenvolvimento do cenário econômico. Adicionalmente, o mesmo procedimento aplicado para os dados brasileiros é aplicado sobre as informações dos Estados Unidos, buscando poder fornecer ao estudo uma base de comparação sobre os dados, tamanho da amostra e estágio de maturidade das respectivas economias. Como conclusão do resultado do trabalho está o fato de que foi possível obter um modelo no qual a inclusão do componente de mercado apresenta menores erros de projeção do que as projeções apenas univariadas, no entanto, os ganhos de projeção não demonstram grande vantagem comparativa a ponto de poder capturar o efeito de antecipação do mercado em relação ao indicador econômico como em alguns casos norte-americanos. Adicionalmente o estudo demonstra que para este trabalho e amostra de dados, mesmo diante de diferentes modelos econométricos de previsão, as projeções univariadas apresentaram resultados similares.

Previsão da inflação no Boletim Focus: uma avaliação

Este trabalho investiga e analisa as diferenças das taxas anuais de inflação realizadas com relação às previsões dos agentes econômicos do mercado para um ano à frente. Os índices analisados foram o IPCA, IPA-M, IGP-M e o IGP-DI. Referente à previsão dos agentes para cada índice, foi feito uma análise estatística e uma análise de séries temporais através do modelo ARIMA. Este último explicou o erro de previsão dos agentes econômicos através de valores passados, ou defasados, do próprio erro de previsão, além dos termos estocásticos.

Compliance com os requisitos de divulgação do IFRS - International Financial Reporting Standards e sua relação com o erro de previsão dos analistas de mercado

O objetivo deste estudo é fazer uma análise da relação entre o erro de previsão dos analistas de mercado quanto à rentabilidade das empresas listadas na BM&FBOVESPA S.A. (Bovespa) e os requerimentos de divulgação do International Financial Reporting Standards (IFRS). Isto foi feito através da regressão do erro de previsão dos analistas, utilizando a metodologia de dados em painel no ano de implantação do IFRS no Brasil, 2010, e, complementarmente em 2012, para referenciamento desses dados. Partindo desse pressuposto, foi determinado o erro de previsão das empresas listadas na Bovespa através de dados de rentabilidade (índice de lucro por ação/earnings per share) previstos e realizados, disponíveis nas bases de dados I/B/E/S Earnings Consensus Information, providos pela plataforma Thomson ONE Investment Banking e Economática Pro®, respectivamente. Os resultados obtidos indicam uma relação negativa entre o erro de previsão e o cumprimento dos requisitos de divulgação do IFRS, ou seja, quanto maior a qualidade nas informações divulgadas, menor o erro de previsão dos analistas. Portanto, esses resultados sustentam a perspectiva de que o grau de cumprimento das normas contábeis é tão ou mais importante do que as próprias normas. Adicionalmente, foi verificado que quando a empresa listada na BM&FBOVESPA é vinculada a Agência Reguladora, seu erro de previsão não é alterado. Por fim, esses resultados sugerem que é importante que haja o aprimoramento dos mecanismos de auditoria das firmas quanto ao cumprimento dos requerimentos normativos de divulgação, tais como: penalidades pela não observância da norma (enforcement), estruturas de governança corporativa e auditorias interna e externa.