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Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks
(Pergamon-Elsevier Science Ltd, 2016)
There has been a growing demand on energy sector for short-term predictions of energy resources to support the planning and management of electricity generation and distribution systems. The purpose of this work is ...
Time series forecasting based on ensemble learning methods applied to agribusiness, epidemiology, energy demand, and renewable energy
(Pontifícia Universidade Católica do ParanáPato BrancoBrasilPrograma de Pós-Graduação em Egenharia de Produção e SistemasPUCPR, 2021-12-03)
Time series forecasting and analysis are helpful in the decision-making process. However, exogenous factors, nonlinearities, and seasonality make developing efficient forecasting models challenging. In this context, the ...
Grid-based simulation method for spatial electric load forecasting using power-law distribution with fractal exponent
(2016-06-01)
A grid-based simulation method to forecast the spatial growth of load density in a distribution utility service zone is presented. The future load density is simulated considering a city's dynamic growth. A power-law ...
Towards assessing the electricity demand in Brazil: Data-driven analysis and ensemble learning models
(2020-01-01)
The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, ...
Forecasting Electric Load Demand through Advanced Statistical Techniques
(Institute of Physics Publishing, 2020-01-07)
Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate ...
Wind power forecast using neural networks: Tuning with optimization techniques and error analysis
(2020-03-01)
The increased integration of wind power into the power system implies many challenges to the network operators, mainly due to the hard to predict and variability of wind power generation. Thus, an accurate wind power ...