Spatial electric load forecasting using a local movement approach

An agent based model for spatial electric load forecasting using a local movement approach for the spatiotemporal allocation of the new loads in the service zone is presented. The density of electrical load for each of the major consumer classes in each sub-zone is used as the current state of the agents. The spatial growth is simulated with a walking agent who starts his path in one of the activity centers of the city and goes to the limits of the city following a radial path depending on the different load levels. A series of update rules are established to simulate the S growth behavior and the complementarity between classes. The results are presented in future load density maps. The tests in a real system from a mid-size city show a high rate of success when compared with other techniques. The most important features of this methodology are the need for few data and the simplicity of the algorithm, allowing for future scalability. © 2009 IEEE.

Spatial load forecasting using a demand propagation approach

A method for spatial electric load forecasting using multi-agent systems, especially suited to simulate the local effect of special loads in distribution systems is presented. The method based on multi-agent systems uses two kinds of agents: reactive and proactive. The reactive agents represent each sub-zone in the service zone, characterizing each one with their corresponding load level, represented in a real number, and their relationships with other sub-zones represented in development probabilities. The proactive agent carry the new load expected to be allocated because of the new special load, this agent distribute the new load in a propagation pattern. The results are presented with maps of future expected load levels in the service zone. The method is tested with data from a mid-size city real distribution system, simulating the effect of a load with attraction and repulsion attributes. The method presents good results and performance. © 2011 IEEE.

Modelo agrometeorológico na estimativa da produtividade de duas variedades de café arábica considerando a variabilidade espacial

The purpose of this study was to estimate the varieties of coffee Arabic Catucáand Catuaí productivity, in Zona da Mata, Minas Gerais, through agrometeorological mathematical models, considering the spatial variability of productivity in the area. The samplings were georrefered building an irregular grid, totalizing 50 samplings per area. After that, geostatic analysis was made to quantify spatial dependence degree of the real values and the estimated productivity. According to the classification, the models superestimated the productivity for the two varieties.

Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.

Short-term multinodal load forecasting in distribution systems using general regression neural networks

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE.

Avaliação dos dados decendiais de precipitação e temperatura máxima e mínima do ar simulados pelo modelo ECMWF para o estado de São Paulo

The weather and climate has a direct influence in agriculture, it affects all stages of farming, since soil preparation to harvest. Meteorological data derived from automatic or conventional weather stations are used to monitor these effects. These meteorological data has problems like difficulty of data access and low density of meteorological stations in Brazil. Meteorological data from atmospheric models, such as ECMWF (European Center for Medium-Range Weather Forecast) can be an alternative. Thus, the aim of this study was to compare 10-day period precipitation, maximum and minimum air temperature data from the ECMWF model with interpolated maps from 33 weather stations in Sao Paulo state between 2005 and 2010 and generate statistical maps pixel by pixel. Statistical index showed spatially satisfactory (most of the results with R 2 > 0.60, d > 0.7, RMSE < 5°C and < 50 mm; Es < 5°C and < 24 mm) in period and ECMWF model can be recommended for use in the Sao Paulo state.

Considering Urban Dynamics in spatial electric load forecasting

When dealing with spatio-temporal simulations of load growth inside a service zone, one of the most important problems faced by a Distribution Utility is how to represent the different relationships among different areas. A new load in a certain part of the city could modify the load growth in other parts of the city, even outside of its radius of influence. These interactions are called Urban Dynamics. This work aims to discuss how to implement Urban Dynamics considerations into the spatial electric load forecasting simulations using multi-agent simulations. To explain the approach, three examples are introduced, including the effect of an attraction load, the effect of a repulsive load, and the effect of several attraction/repulsive loads at the same time when considering the natural load growth. © 2012 IEEE.

Deposition and photo-induced electrical resistivity of dip-coated NiO thin films from a precipitation process

Thin films of the semiconductor NiO are deposited using a straightforward combination of simple and versatile techniques: the co-precipitation in aqueous media along with the dip- coating process. The obtained material is characterized by gravimetric/differential thermal analysis (TG-DTA) and X-ray diffraction technique. TG curve shows 30 % of total mass loss, whereas DTA indicates the formation of the NiO phase about 578 K (305 C). X-ray diffraction (XRD) data confirms the FCC crystalline phase of NiO, whose crystallinity increases with thermal annealing temperature. UV-Vis optical absorption measurements are carried out for films deposited on quartz substrate in order to avoid the masking of bandgap evaluation by substrate spectra overlapping. The evaluated bandgap is about 3.0 eV. Current-voltage (I-V) curves measured for different temperatures as well as the temperature-dependent resistivity data show typical semiconductor behavior with the resistivity increasing with the decreasing of temperature. The Arrhenius plot reveals a level 233 meV above the conduction band top, which was attributed to Ni2+ vacancy level, responsible for the p-type electrical nature of NiO, even in undoped samples. Light irradiation on the films leads to a remarkable behavior, because above bandgap light induced a resistivity increase, despite the electron-hole generation. This performance was associated with excitation of the Ni 2+ vacancy level, due to the proximity between energy levels. © 2012 Springer Science+Business Media New York.

Methodology for forecasting electric power demand

Includes bibliography

Forecasting tourist arrivals in the Caribbean

Includes bibliography

Short-term energy forecasting using time series techniques: Chile, a case study

Includes bibliography

Clima e turismo no município de Botucatu-SP

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)