Panel semiparametric quantile regression neural network for electricity consumption forecasting

Grey Prediction Model for Forecasting Electricity consumption

Accurate prediction of the future electricity consumption is crucial for production electricity management. Since the storage of electrical energy is very difficult, reliable and accurate prediction of power consumption is important. Different approaches for this purpose were used. In this paper, Grey model (1,1) based on grey system theory has been used for forecasting results. Annual electric...

Bayesian semiparametric additive quantile regression

Quantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation relies on assuming the asymmetric Laplace distribution as auxiliary error distribution that yields po...

Forecasting Electricity Consumption for Pakistan

Now-a-days, different sectors of the economy are being significantly affected by the electricity variable. In this research, we analyzed the monthly electricity consumption in Pakistan for the period of January 1990 through December 2011, using linear and non linear modeling techniques. They include ARIMA, Seasonal ARIMA (SARIMA) and ARCH/GARCH models. Electricity consumption model reveals a si...

Artificial neural networks for electricity consumption forecasting considering climatic factors

This work develops Artificial Neural Networks (ANN) models applied to predict the consumption forecasting considering climatic factors. It is intended to verify the influence of climatic factors on the electricity consumption forecasting through the ANN. The case study is applied in the Campinas city, Brazil. This work used Perceptron and Backpropagation ANN models. The specific goal is compari...

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