Effect of flow forecasting quality on benefits of reservoir operation - a case study for the Geheyan reservoir (China)

This paper presents a methodology to determine the effect of flow forecasting quality on the benefits of reservoir operation. The benefits are calculated in terms of the electricity generated, and the quality of the flow forecasting is defined in terms of lead time and accuracy of the forecasts. In order to determine such an effect, an optimization model for reservoir operation was developed which consists of two sub-models: a long-term (monthly) and a short-term (daily) optimization sub-model. A methodology was developed to couple these two sub-models, so that both short-term benefits (time span in the order of the flow forecasting lead time) and long-term benefits (one year) were considered and balanced. Both sub-models use Discretized Dynamic Programming (DDP) as their optimization algorithms. The Geheyan reservoir on the Qingjiang River in China was taken as case study. Observed (from the 1997 hydrological year) and forecasted flow series were used to calculate the benefits. Forecasted flow series were created by adding noises to the observed series. Different magnitudes of noise reflected different levels of forecasting accuracies. The results reveal, first of all, a threshold lead time of 33 days, beyond which further extension of the forecasting lead time will not lead to a significant increase in benefits. Secondly, for lead times shorter than 33 days, a longer lead time will generally lead to a higher benefit. Thirdly, a perfect inflow forecasting with a lead time of 4 days will realize 87% of the theoretical maximum electricity generated in one year. Fourthly, for a certain lead time, more accurate forecasting leads to higher benefits. For inflow forecasting with a fixed lead time of 4 days and different forecasting accuracies, the benefits can increase by 5 to 9% compared to the actual operation results. It is concluded that the definition of the appropriate lead time will depend mainly on the physical conditions of the basin and on the characteristics of the reservoir. The derived threshold lead time (33 days) gives a theoretical upper limit for the extension of forecasting lead time. Criteria for the appropriate forecasting accuracy for a specific feasible lead-time should be defined from the benefit-accuracy relationship, starting from setting a preferred benefit level, in terms of percentage of the theoretical maximum. Inflow forecasting with a higher accuracy does not always increase the benefits, because these also depend on the operation strategies of the reservoir.\u

Enhancing Multi-Scale Simulations of Charge and Exciton Transfer with Machine Learning Methods

Die theoretische Untersuchung von Ladungs- und Energietransferphänomenen erfordert rechenintensive Multi-Skalen-Simulationen, die eine quantenmechanische und eine klassische Beschreibung kombinieren. Diese Arbeit strebt eine Integration von datengesteuerten Methoden des maschinellen Lernens in den workflow solcher Simulationen an. Indem der kostspielige quantenchemische Teil ersetzt wird, können diese beschleunigt werden. Es wird gezeigt, dass einfache und kompakte Kernel-Regressionsmodelle in der Lage sind, nicht-adiabatische Molekulardynamiksimulationen durch die Vorhersage der Elemente des Transfer-Hamiltonian voranzutreiben. Referenzdaten einer semiempirischen Methode wurden genau reproduziert. Diese Modelle führen jedoch nicht zu einer Beschleunigung und die Menge der Trainingsdaten ist deutlich eingeschränkt. Dies könnte das Training komplexerer und größerer Moleküle erschweren. Im Gegensatz dazu bieten neuronale Netze eine erhebliche Effizienzsteigerung im Vergleich zu einer semiempirischen Referenzmethode und eine noch größere Beschleunigung für genauere quantenmechanische Methoden. Gleichzeitig besteht keine Begrenzung für die Größe der Trainingsdatenmenge. Außerdem ermöglichen die Modelle die gleichzeitige Vorhersage von Transfer-Hamiltonianelementen und deren Ableitungen, was für die explizite Behandlung des Relaxationsprozesses und die korrekte Neuskalierung der atomaren Impulse mit nicht-adiabatischen Kopplungsvektoren notwendig ist. Darüber hinaus wurde die Methodik auf den Exzitonentransfer ausgeweitet. Der Einfluss von Kurzstreckeneffekten in supermolekularen Kopplungen wurde untersucht und ein Diabatisierungsschema für genauere und zuverlässigere Berechnungen wurde implementiert. Die Anwendung von neuronalen Netzen auf den Exzitonentransfer in Anthracen konnte die experimentell ermittelten Diffusionskonstanten reproduzieren und zeigte einen stark lokalisierten Transfer. Schließlich wurden die Entwicklungen dieser Arbeit kombiniert und resultierten in der Anwendung von Exzitonentransfer-Simulationen auf den Lichtsammelkomplex II (LH2) von Purpurbakterien. Dieser biologische Komplex enthält Chromophore, die in zwei Ringen angeordnet sind (B800, B850). Bisher war es nur in einer Studie möglich, eine einzige Simulation von 300 fs Länge durchzuführen. Dies ist auf die enormen Rechenkosten solcher Simulationen zurückzuführen, die durch den in dieser Arbeit entwickelten datengesteuerten Ansatz verringert werden. Der Transfer in beiden Ringen wurde für 10 ps in jeweils 1000 Trajektorien simuliert, was mit einem vertretbaren Aufwand an Ressourcen realisierbar war. Die Exzitonen im B800-Ring waren stark lokalisiert und wurden in diskreten Sprüngen übertragen, während die B850-Chromophore einen kohärenten Transport und eine Delokalisierung des Exzitons über mehrere Moleküle ermöglichen. Die abgeschätzten Diffusionskonstanten für den Transfer in beiden Ringen waren deutlich größer als die von organischen Halbleitermaterialien. Jetzt werden groß angelegte Simulationen mit dem Ziel möglich, den gesamten Lichtsammelprozess in photosynthetisch aktiven Organismen von der Absorption bis zur Ladungstrennung aufzuklären

Superstar Returns

We study long-term returns on residential real estate in 27 "superstar" cities in 15 countries over 150 years. We find that total returns in superstar cities are close to 100 basis points lower per year than in the rest of the country. House prices tend to grow faster in the superstars, but rent returns are substantially greater outside the big agglomerations, resulting in higher long-run total returns. The excess returns outside the superstars can be rationalized as a compensation for risk, especially for higher co-variance with income growth and lower liquidity. Superstar real estate is comparatively safe

Superstar Returns

We study long-term returns on residential real estate in twenty-seven “superstar” cities in fifteen countries over 150 years. We find that total returns in superstar cities are close to 100 basis points lower per year than in the rest of the country. House prices tend to grow faster in the superstars, but rent returns are substantially greater outside the big agglomerations, resulting in higher long-run total returns. The excess returns outside the superstars can be rationalized as a compensation for risk, especially for higher covariance with income growth and lower liquidity. Superstar real estate is comparatively safe

Housing Returns in Big and Small Cities

Houses are the largest asset for most households in the United States, as is the case in many other countries as well. Within countries, there is substantial regional variation in house prices—compare real estate values in Manhattan, New York City, with those in Manhattan, Kansas, for example. But what about returns on investment? Are long-run returns on real estate investment—the sum of price appreciation and rental income flows—higher in superstar cities like New York than in the rest of the country? In this blog post, we present new and potentially surprising insights from research comparing long-run returns on residential real estate in a nation’s largest cities to those experienced in the rest of the country (Amaral et al., 2021), covering the U.S. and fourteen other advanced economies over the past century

Interest Rates and the Spatial Polarization of Housing Markets

Rising within-country differences in house values are much debated trend in the U.S. and internationally. Using new long-run regional data for 15 advanced economies, we first show that standard explanations linking growing price dispersion to rent dispersion are contradicted by an important stylized fact: rent dispersion has increased far less than price dispersion. We then propose a new explanation: a uniform decline in real risk-free interest rates can have heterogeneous spatial effects on house values. Falling real safe rates disproportionately push up prices in large agglomerations where initial rent-price ratios are low, leading to housing market polarization on the national level