Abstract Nowadays, renewable energies are important sources for supplying electric power demand and a key entity of future energy markets. Therefore, wind producers (WPPs) in most the systems world have role. On other hand, speed uncertainty makes WPPs deferent generators, which turn causes adequate bidding strategies, that leads to market rules, functional abilities turbines penetrate market. ...

Although the machine-learning model demonstrates high accuracy in wind speed prediction, it struggles to accurately depict fluctuation range of predicted values due inherent uncertainty sequences. To address this limitation and enhance reliability, we propose an effective interval prediction that combines twin support vector regression (TSVR), variational mode decomposition (VMD), slime mould a...

In order to defraud state subsidies, some unscrupulous users use improper means steal photovoltaic (PV) power. This behavior brings potential safety hazards grid-connected operations. this paper, a power-stealing identification method based on similar-day clustering and interval prediction of the quantile regression model for long short-term memory neural network (QRLSTM) is proposed. First, da...

A test of a new Bayesian approach to solar flare prediction (Wheatland 2004a) is presented. The approach uses the past history of flaring together with phenomenological rules of flare statistics to make a prediction for the probability of occurrence of a large flare within an interval of time, or to refine an initial prediction (which may incorporate other information). The test of the method i...

Maximum likelihood predictive densities (MLPD) for the inverse Gaussian distribution are derived for the cases of one or both parameters unknown. They are then applied to obtain estimators of the reliability function and prediction or shortest prediction intervals for a future observation. Comparisons with the existing likelihood or frequentist methods show that the MLPD estimators of reliabili...

Introduction With prediction markets growing in number and in prominence in various domains, the construction of a modeling framework for the behavior of prices on traded contracts has become an increasingly important endeavor. In this paper, we present such a theoretical framework, as we attempt to use mar-tingale theory in the analysis of prediction market price fluctuations. The application ...

Continuous multi-interval prediction (CMIP) is used to continuously predict the trend of a data stream based on various intervals simultaneously. The continuous integrated hierarchical temporal memory (CIHTM) network performs well in CMIP. However, it is not suitable for CMIP in real-time mode, especially when the number of prediction intervals is increased. In this paper, we propose a real-tim...