Neural networks have been shown to be effective in modelling time series, with applications in the forecasting of electricity consumption. In applying neural networks to weekly electricity consumption data, several issues, such as selection of network architecture, network structure and input structure need to be addressed. This paper addresses these issues in relation to the current application and also demonstrates that considerable value is to be gained from incorporating the lessons learned from linear time series modelling into the current nonlinear analysis. Results for national Irish weekly electricity data demonstrate the potential improvements which can be obtained using the neural network approach
This paper deals with so-called feedforward neural network model which we consider from a statistica...
Abstract Smart grids and smart homes are getting people’s attention in the modern era of smart citie...
This paper presents a comparison between parallel linear and parallel neural network models. Paralle...
Neural networks have been shown to be effective in modelling time series, with applications in the f...
This paper examines the application of artificial neural networks (ANNs) to the modelling and foreca...
Accurate electricity demand forecasts are critical for daily operations planning. They influence man...
This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC)...
The recent decade has focused attention more than ever before on the increasing carbon emissions and...
International audienceRecently, there has been a significant emphasis on the forecasting of the elec...
In this study several Artificial Neural Network (ANN) models were experimented to predict electricit...
Forecasting is an essential function in the electricity supply industry. Electricity demand forecast...
The tremendous rise of electrical energy demand worldwide has led to many problems related to effici...
In this study several Artificial Neural Network (ANN) models were experimented to predict electricit...
Power systems require the continuous balance of energy supply and demand for their appropriate funct...
Accurate prediction of electricity demand can bring extensive benefits to any country as the forecas...
This paper deals with so-called feedforward neural network model which we consider from a statistica...
Abstract Smart grids and smart homes are getting people’s attention in the modern era of smart citie...
This paper presents a comparison between parallel linear and parallel neural network models. Paralle...
Neural networks have been shown to be effective in modelling time series, with applications in the f...
This paper examines the application of artificial neural networks (ANNs) to the modelling and foreca...
Accurate electricity demand forecasts are critical for daily operations planning. They influence man...
This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC)...
The recent decade has focused attention more than ever before on the increasing carbon emissions and...
International audienceRecently, there has been a significant emphasis on the forecasting of the elec...
In this study several Artificial Neural Network (ANN) models were experimented to predict electricit...
Forecasting is an essential function in the electricity supply industry. Electricity demand forecast...
The tremendous rise of electrical energy demand worldwide has led to many problems related to effici...
In this study several Artificial Neural Network (ANN) models were experimented to predict electricit...
Power systems require the continuous balance of energy supply and demand for their appropriate funct...
Accurate prediction of electricity demand can bring extensive benefits to any country as the forecas...
This paper deals with so-called feedforward neural network model which we consider from a statistica...
Abstract Smart grids and smart homes are getting people’s attention in the modern era of smart citie...
This paper presents a comparison between parallel linear and parallel neural network models. Paralle...