Analysis of Photovoltaic System Energy Performance Evaluation Method

Documentation of the energy yield of a large photovoltaic (PV) system over a substantial period can be useful to measure a performance guarantee, as an assessment of the health of the system, for verification of a performance model to then be applied to a new system, or for a variety of other purposes. Although the measurement of this performance metric might appear to be straight forward, there are a number of subtleties associated with variations in weather and imperfect data collection that complicate the determination and data analysis. A performance assessment is most valuable when it is completed with a very low uncertainty and when the subtleties are systematically addressed, yet currently no standard exists to guide this process. This report summarizes a draft methodology for an Energy Performance Evaluation Method, the philosophy behind the draft method, and the lessons that were learned by implementing the method

Ensemble-based methods for forecasting census in hospital units

BACKGROUND: The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. METHODS: In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, we use a seasonality adjusted Poisson Autoregressive (PAR) model where the parameter estimates are obtained via conditional maximum likelihood. The number of daily departures is predicted by modeling the probability of departure from the census using logistic regression models that are adjusted for the amount of time spent in the census and incorporate both patient-specific baseline and time varying patient-specific covariate information. We illustrate our approach using neonatal intensive care unit (NICU) data collected at Women & Infants Hospital, Providence RI, which consists of 1001 consecutive NICU admissions between April 1st 2008 and March 31st 2009. RESULTS: Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information. CONCLUSIONS: Forecasting models that utilize patient-specific baseline and time-varying information make the most of data typically available and have the capacity to substantially improve census forecasts

Optimal Waterfowl Hunting Management Strategies for Private Landowners: A Minnesota Case Study

A bio-economic model based upon waterfowl population, habitat, and hunting data in the state of Minnesota is used to examine the optimal management strategy of a waterfowl hunting enterprise on privately owned land. Various state sponsored incentive programs are then analyzed for their effect on hunting and waterfowl equilibrium levels, as well as the economic viability of the hunting enterprise. A waterfowl habitat and maintenance cost reimbursement incentive program is found to be the most effective at inducing additional hunting opportunities in Minnesota, while providing economic incentives for private landowners to actively manage their land

Entwicklung eines wissensbasierenden Betriebsleitrechners fuer verteilte Automatisierungsprozesse in der Abwassertechnik Abschlussbericht

Available from TIB Hannover: D.Dt.F.QN1(8,18) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman