Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Cellular Automaton Approach to Spatial Electric Load Forecasting

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Um sistema de previsão de tempo e de ondas oceânicas para o Atlântico Sul

Um sistema de previsão numérica de tempo e de ondas oceânicas (SPTO) que possa ser operacionalizado no Atlântico Sul é proposto. O SPTO é composto por um modelo atmosférico de área limitada (MAL) e um modelo de ondas de superfície do oceano geradas pelo vento, aplicado em duas versões: uma de malha grossa (MPOMG) e outra de malha fina (MPOMF). O MPOMG abrange uma área de 10(6) km², e tem como finalidade gerar e propagar ondas em regiões remotas à costa brasileira. O MPOMF é aplicado em um domínio 10(4) km² com alta resolução, incorporando irregularidades batimétricas e com as condições iniciais e de fronteiras fomecidas pelo MPOMG. Os modelos utilizam dados de vento à 10 m acima da superfície do oceano. Os arquivos de vento, contendo a evolução espacial e temporais são gerados pelo MAL. Um exemplo de um evento real ocorrido no período de 9 a 11 de agosto de 1988 é apresentado utilizando o acoplamento proposto.

Uma experiência com modelo estatístico (MOS) para a previsão da temperatura mínima diária do ar

Uma equação de regressão múltipla MOS (da sigla em inglês para Model Output Statistics), para previsão da temperatura mínima diária do ar na cidade de Bauru, estado de São Paulo, é desenvolvida. A equação de regressão múltipla, obtida usando análise de regressão stepwise, tem quatro preditores, três do modelo numérico global do Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) e um observacional da estação meteorológica do Instituto de Pesquisas Meteorológicas (IPMet), Bauru. Os preditores são prognósticos para 24 horas do modelo global, válidos para 00:00GMT, da temperatura em 1000hPa, vento meridional em 850hPa e umidade relativa em 1000hPa, e temperatura observada às 18:00GMT. Esses quatro preditores explicam, aproximadamente, 80% da variância total do preditando, com erro quadrático médio de 1,4°C, que é aproximadamente metade do desvio padrão da temperatura mínima diária do ar observada na estação do IPMet. Uma verificação da equação MOS com uma amostra independente de 47 casos mostra que a previsão não se deteriora significativamente quando o preditor observacional for desconsiderado. A equação MOS, com ou sem esse preditor, produz previsões com erro absoluto menor do que 1,5°C em 70% dos casos examinados. Este resultado encoraja a utilização da técnica MOS para previsão operacional da temperatura mínima e seu desenvolvimento para outros elementos do tempo e outras localidades.

Spatial electric load forecasting using an evolutionary heuristic

A method for spatial electric load forecasting using elements from evolutionary algorithms is presented. The method uses concepts from knowledge extraction algorithms and linguistic rules' representation to characterize the preferences for land use into a spatial database. The future land use preferences in undeveloped zones in the electrical utility service area are determined using an evolutionary heuristic, which considers a stochastic behavior by crossing over similar rules. The method considers development of new zones and also redevelopment of existing ones. The results are presented in future preference maps. The tests in a real system from a midsized city show a high rate of success when results are compared with information gathered from the utility planning department. The most important features of this method are the need for few data and the simplicity of the algorithm, allowing for future scalability.

Implementação de um sistema Collaborative Planning, Forecasting, and Replenishment em uma grande rede de fast food por meio de um prestador de serviços logísticos

A gestão colaborativa é, atualmente, um elemento-chave no contexto da gestão da cadeia de suprimentos. Neste artigo, o tema é abordado mediante a análise de um caso real, em que uma grande rede mundial de fast-food e seu prestador de serviço logístico (PSL) trabalharam conjuntamente no Brasil em um projeto-piloto para a implementação de um collaborative planning, forecasting, and replenishment (CPFR). O trabalho faz uso de uma metodologia de pesquisa-ação e apresenta as principais variáveis que influenciaram o projeto, abordando os processos necessários para a implementação e os pontos que favorecem o CPFR. Com base no caso estudado, o trabalho apresenta um conjunto de propostas sobre o papel dos agentes da cadeia em projetos dessa natureza. A gestão da cadeia de suprimentos por intermédio da coordenação direta de um PSL também permite demonstrar as possibilidades e dificuldades desse sistema, contribuindo com a visão colaborativa na cadeia de suprimentos a partir da relação entre seus agentes.

Sea waves generated by extratropical cyclones in the South Atlantic Ocean: Hindcast and validation against altimeter data

This work presents analyses of the atmospheric conditions and the hindcast of the surface wave field when six extratropical cyclones formed and displaced over the South Atlantic Ocean (10degreesN, 60degreesS; 75degreesW, 15degreesE) between April and September 1999. These events caused high sea waves associated with hazardous conditions along the south and southeast coast of Brazil. The meteorological composite fields for these cyclones show a strong near-surface wind velocity (up to 14 m s(-1)) during its mature phase. The sea-state wave hindcast was obtained using a third-generation wave model forced by the 10-m above ground level wind field from the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis dataset. Closer to the south and southeast Brazilian coast, the hindcast results showed significant wave heights of up to 5 m in some of the events. The wave hindcast results for the significant wave height were compared against satellite altimeter data at 6 h intervals. The statistical index showed a systematic underestimation of the significant wave height by 0.5 m. The correlation between wave hindcast and altimeter measurements was greater than 90%, showing a good phase reproduction by the wave model.

The use of simulation for forecasting the number of docks necessary in an expedition department

The simulation is a very powerful tool to develop more efficient systems, hence it is been widely used with the goal of productivity improvement. Its results, if compared with other methods, are not always optimum; however, if the experiment is rightly elaborated, its results will represent the real situation, enabling its use with a good level of reliability. This work used the simulation (through the ProModel (R) software) in order to study, understand, model and improve the expenditure system of an enterprise, with a premise of keeping the production-delivery flow considering quick, controlled and reliable conditions.

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

Multi-Agent Framework for Spatial Load Forecasting

A multi-agent framework for spatial electric load forecasting, especially suited to simulate the different dynamics involved on distribution systems, is presented. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented as development probabilities. With this setting, different kind of agents can be developed to simulate the growth pattern of the loads in distribution systems. This paper presents two different kinds of agents to simulate different situations, presenting some promissory results.

Convective storm initiation in a moist tropical environment

Radar and satellite data from the Tropical Rainfall Measuring Mission-Large-Scale Biosphere-Atmosphere (TRMM-LBA) project have been examined to determine causes for convective storm initiation in the southwest Amazon region. The locations and times of storm initiation were based on the National Center for Atmospheric Research (NCAR) S-band dual-polarization Doppler radar (S-Pol). Both the radar and the Geostationary Operational Environmental Satellite-8 (GOES-8) visible data were used to identify cold pools produced by convective precipitation. These data along with high-resolution topographic data were used to determine possible convective storm triggering mechanisms. The terrain elevation varied from 100 to 600 m. Tropical forests cover the area with numerous clear-cut areas used for cattle grazing and farming. This paper presents the results from 5 February 1999. A total of 315 storms were initiated within 130 km of the S-Pol radar. This day was classified as a weak monsoon regime where convection developed in response to the diurnal cycle of solar heating. Scattered shallow cumulus during the morning developed into deep convection by early afternoon. Storm initiation began about 1100 LST and peaked around 1500-1600 LST. The causes of storm initiation were classified into four categories. The most common initiation mechanism was caused by forced lifting by a gust front (GF; 36%). Forcing by terrain (>300 m) without any other triggering mechanism accounted for 21% of the initiations and colliding GFs accounted for 16%. For the remaining 27% a triggering mechanism was not identified. Examination of all days during TRMM-LBA showed that this one detailed study day was representative of many days. A conceptual model of storm initiation and evolution is presented. The results of this study should have implications for other locations when synoptic-scale forcing mechanisms are at a minimum. These results should also have implications for very short-period forecasting techniques in any location where terrain, GFs, and colliding boundaries influence storm evolution.

A radar reflectivity-runoff model for use in flood warning

This model connects directly the radar reflectivity data and hydrological variable runoff. The catchment is discretized in pixels (4 Km × 4 Km) with the same resolution of the CAPPI. Careful discretization is made so that every grid catchment pixel corresponds precisely to CAPPI grid cell. The basin is assumed a linear system and also time invariant. The forecast technique takes advantage of spatial and temporal resolutions obtained by the radar. The method uses only the measurements of the factor reflectivity distribution observed over the catchment area without using the reflectivity - rainfall rate transformation by the conventional Z-R relationships. The reflectivity values in each catchment pixel are translated to a gauging station by using a transfer function. This transfer function represents the travel time of the superficial water flowing through pixels in the drainage direction ending at the gauging station. The parameters used to compute the transfer function are concentration time and the physiographic catchment characteristics. -from Authors

A case study of the 9 August 1988 South Atlantic storm: Numerical simulations of the wave activity

During 9-11 August 1988, a cyclone developed over Uruguay in the lee of the Andes Mountains and moved over the South Atlantic Ocean, where it redeveloped into an intense storm. This storm was responsible for unusual wave activity along the Brazilian shoreline from 22° to 32°S. The Brazilian news media reported the loss of at least one life, waves of 3 m and higher, and the disappearance of a drainage pipe, which weighed 8000 kg, off the shores of Rio de Janeiro. In this paper, the evolution of this intense storm and the associated ocean wave response is studied through European Centre for Medium-Range Weather Forecasts analyses, a hydrostatic limited-area meteorological model, and a second-generation prognostic wave model. The atmospheric model results indicated the presence of a long-lived and large fetch with surface wind velocities higher than 12 m s -1 directed toward the coast. Some areas with velocities of 20 m s -1 were embedded in the fetch. The wave model forced by this wind field was able to simulate waves with a significant height of 8 m far from the coast and about 4 m in regions very close to the Brazilian coast in agreement with the occurrence reported at Rio de Janeiro. The swell propagation toward the coast of Rio de Janeiro was obstructed by a northeastward 10-m wind during the first 24-h period of the model's integration. During the second 24-h period, the fetch was still large and strong, but the obstacle was removed by a counterclockwise rotation of wind direction favoring the swell and windsea propagation toward the Rio de Janeiro coast.