Forecasting the electricity load from one day to one week ahead for the Spanish system operator

One-Day-Ahead Load Forecasting using nonlinear Kalman filtering algorithms

In this paper, we consider the problem of 24-hour ahead short-term load forecasting; the formulation is based on the nonlinear Kalman filtering. Our formulation takes into account weather conditions as well as previous trends. Effects of weather as well as prior consumptions are nonlinear functions; hence our choice. We compare our proposed method with the standard Kalman filtering approach and...

One Day Ahead Load Forecasting Using Recurrent Neural Network

This paper presents short term load forecasting (STLF) in Java Island using recurrent neural network (RNN). The simple one of RNN is Elman, it has one hidden layer and suitable used in time series prediction. It can learn an input-output mapping which is nonlinear. The Elman RNN was proposed for one day a head forecasting, with interval time 30 minutes. Training model divided into weekday, week...

One Day Ahead Stream Flow Forecasting

One step ahead forecasting using

When dealing with time series the one step ahead forecasting problem based on experimental data is the problem of estimating the autoregression function of the underlying process When minimizing the expected forecast ing error is the main goal the exible approach has to be used to be able to adjust the complexity of the model to the complexity of the data Multilay ered perceptrons are a popular...

Application of a New Hybrid Method for Day-Ahead Energy Price Forecasting in Iranian Electricity Market

Abstract- In a typical competitive electricity market, a large number of short-term and long-term contracts are set on basis of energy price by an Independent System Operator (ISO). Under such circumstances, accurate electricity price forecasting can play a significant role in improving the more reasonable bidding strategies adopted by the electricity market participants. So, they cannot only r...