LES validation of urban flow, part I: flow statistics and frequency distributions

Essential prerequisites for a thorough model evaluation are the availability of problem-specific, quality-controlled reference data and the use of model-specific comparison methods. The work presented here is motivated by the striking lack of proportion between the increasing use of large-eddy simulation (LES) as a standard technique in micro-meteorology and wind engineering and the level of scrutiny that is commonly applied to assess the quality of results obtained. We propose and apply an in-depth, multi-level validation concept that is specifically targeted at the time-dependency of mechanically induced shear-layer turbulence. Near-surface isothermal turbulent flow in a densely built-up city serves as the test scenario for the approach. High-resolution LES data are evaluated based on a comprehensive database of boundary-layer wind-tunnel measurements. From an exploratory data analysis of mean flow and turbulence statistics, a high level of agreement between simulation and experiment is apparent. Inspecting frequency distributions of the underlying instantaneous data proves to be necessary for a more rigorous assessment of the overall prediction quality. From velocity histograms local accuracy limitations due to a comparatively coarse building representation as well as particular strengths of the model to capture complex urban flow features with sufficient accuracy are readily determined. However, the analysis shows that further crucial information about the physical validity of the LES needs to be obtained through the comparison of eddy statistics, which is focused on in part II. Compared with methods that rely on single figures of merit, the multi-level validation strategy presented here supports conclusions about the simulation quality and the model's fitness for its intended range of application through a deeper understanding of the unsteady structure of the flow

A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST

Electrical load forecast is an important part of the power system energy management system. Reliable load forecast technique will help the electric utility to make unit commitment decisions, reduce spinning reserve capacity, and schedule device maintenance plan properly. Thus, besides being a key element in reducing the generation cost, power load forecast is an essential procedure in enhancing the reliability of the power systems. Generally speaking, power systems worldwide are using load forecast as an essential part of off-line network analysis. This is in order to determine the status of the system, and the necessity to implement corrective actions, such as load shedding, power purchases or using peaking units. Short term load forecast (STLF), in terms of one-hour ahead, 24-hours ahead, and 168-hours ahead is a necessary daily task for power dispatch. Its accuracy will significantly affect the cost of generation and the reliability of the system. The majority of the single variable based techniques are using autoregressive-moving average (ARMA) model to solve the STLF problem. In this thesis, a new AR algorithm especially designed for long data records as a solution to STLF problem is proposed. The proposed AR-based algorithm divides long data record into short segments and searches for the AR coefficients that simultaneously model the data with the least means squared errors. In order to verify the proposed algorithm as a solution to STLF problem, its performance is compared with other AR-based algorithms, like Burg and the seasonal Box-Jenkins ARIMA (SARIMA). In addition to the parametric algorithms, the comparison is extended towards artificial neural networks (ANN). Three years data power demand record collected by NEMMCO in four Australian states, NSW, QLD, SA, and VIC, between the beginning of 2005 and the end of 2007 are used for the comparison. The results show the potential of the proposed algorithm as a reliable solution to STLF

Técnicas de taxa de transmissão adaptativa para redes de sensores sem fio

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Metrologia Científica e Industrial, Florianópolis, 2010.A utilização de redes de sensores sem fio, nos últimos anos, vem ganhando espaço, tornando-se uma tendência para a área de metrologia. A Fundação CERTI, visto a potencialidade de tais redes, está desenvolvendo o Projeto SensIInt, fundamentado na conceituação, modelagem e prototipagem de um "Sistema Modular de Sensores Inteligentes e Integráveis". Apesar do avanço expressivo na área, tais redes apresentam uma série de desafios, dentre os quais: aumentar a eficiência energética e diminuir custos da rede. Assim sendo, este estudo atuará nas soluções destas questões, focando para isso na redução das transmissões. Agora, entretanto, o problema é como reduzir o número de transmissões e o volume de dados transmitidos sem causar grandes impactos na incerteza de medição. Para alcançar os objetivos almejados, foi proposto o uso de técnicas de taxa de transmissão adaptativa. Tais técnicas foram testadas e avaliadas dentro da aplicação-teste do Projeto SensIInt, monitoramento ambiental, por meio de simulações computacionais. Os resultados são realmente favoráveis, já que as técnicas propostas ofereceram uma economia de mais de 90% no número de transmissões, sem aumentar a incerteza de medição final. Para o Projeto SensIInt a utilização de técnicas de taxa de transmissão adaptativa resulta no aumento da eficiência energética da rede e no corte de custos, possibilitando a otimização do sistema

A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST

Electrical load forecast is an important part of the power system energy management system. Reliable load forecast technique will help the electric utility to make unit commitment decisions, reduce spinning reserve capacity, and schedule device maintenance plan properly. Thus, besides being a key element in reducing the generation cost, power load forecast is an essential procedure in enhancing the reliability of the power systems. Generally speaking, power systems worldwide are using load forecast as an essential part of off-line network analysis. This is in order to determine the status of the system, and the necessity to implement corrective actions, such as load shedding, power purchases or using peaking units. Short term load forecast (STLF), in terms of one-hour ahead, 24-hours ahead, and 168-hours ahead is a necessary daily task for power dispatch. Its accuracy will significantly affect the cost of generation and the reliability of the system. The majority of the single variable based techniques are using autoregressive-moving average (ARMA) model to solve the STLF problem. In this thesis, a new AR algorithm especially designed for long data records as a solution to STLF problem is proposed. The proposed AR-based algorithm divides long data record into short segments and searches for the AR coefficients that simultaneously model the data with the least means squared errors. In order to verify the proposed algorithm as a solution to STLF problem, its performance is compared with other AR-based algorithms, like Burg and the seasonal Box-Jenkins ARIMA (SARIMA). In addition to the parametric algorithms, the comparison is extended towards artificial neural networks (ANN). Three years data power demand record collected by NEMMCO in four Australian states, NSW, QLD, SA, and VIC, between the beginning of 2005 and the end of 2007 are used for the comparison. The results show the potential of the proposed algorithm as a reliable solution to STLF

Error measures for resampled irregular data

Abstract—With resampling, a regularly sampled signal is extracted from observations which are irregularly spaced in time. Resampling methods can be divided into simple and complex methods. Simple methods such as Sample&Hold (S&H) and Nearest Neighbor Resampling (NNR) use only one irregular sample for one resampled observation. A theoretical analysis of the simple methods is given. The various resampling methods are compared using the new error measure SD: the spectral distortion at interval. SD is zero when the time domain properties of the signal are conserved. Using the time domain approach, an antialiasing filter is no longer necessary: the best possible estimates are obtained by using the data themselves. In the frequency domain approach, both allowing aliasing and applying antialiasing leads to distortions in the spectrum. The error measure SD has been compared to the reconstruction error. A small reconstruction error does not necessarily result in an accurate estimate of the statistical signal properties as expressed by SD. Index Terms—Interpolation, signal reconstruction, signal sampling, spectral analysis, time domain analysis. I