Forecasting One Day Ahead Stream Flow Using Support Vector Regression

One Day Ahead Stream Flow Forecasting

Solar Power Forecasting Using Support Vector Regression

Generation and load balance is required in the economic scheduling of generating units in the smart grid. Variable energy generations particularly from wind and solar energy resources are witnessing a rapid boost, and, it is anticipated that with a certain level of their penetration, they become noteworthy sources of uncertainty. As in the case of load demand, energy forecasting can also be use...

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...

Multi-Step Ahead Forecasting of Road Condition Using Least Squares Support Vector Regression

Network-level multi-step road condition forecasting is an important step in accurate road maintenance planning, where correct maintenance activities are defined in place and time of road networks. Forecasting methods have developed from engineering models to non-linear machine learning methods that make use of the collected condition and traffic data of the road network. Least Squares Support V...