A Neural Network Model for Forecasting CO2 Emission

We present a novel approach, using a Generalized Regression Neural Networks (GRNN) ensemble to the forecasting of time series and future volatility.. The remainder of this paper

Three NN-based predictors have been developed and applied to short-term forecasting of daily peak load, total daily energy, and hourly daily load, respectively.. Three months of

It includes all input units em- ployed in the representation for the HNM, except the unit which represents the load at 1 week and 24 h be- hind the hour whose load is to be

[10] describe the third genera- tion of an hourly short-term load forecasting system, named artificial neural network short-term load fore- caster (ANNSTLF).. Its architecture

In this paper, genetic algorithm based artificial neural network hybrid prediction model is proposed to foretell surface roughness and tool wear.. A multiple

For the design of the Dumb and Deaf Special Interface (DDSI) Expert Systems, the neural network model is implemented.. Architecture of

Using the ANN model developed, rainfall from 1 to 6 h was forecasted for 75 rain gauge stations (as forecast point) in Bangkok, Thailand, using continuous hourly rainfall data...

In this paper Ontario electricity market is used to predict one hour a head electricity price that is a very important issue for power generation and electricity markets,