In this paper different prediction models based on methods of the artificial intelligence are studied for wind power prediction of single wind farms. The used methods are neural networks, mixture of experts, support vector machines and nearest neighbour search with a superior particle swarm optimization. We build for day-ahead prediction and for short-term prediction with a prediction horizon o...

This paper considers the problem of a robot navigating in a crowded or congested environment. A robot operating in such an environment can get easily blocked by moving humans and other objects. To deal with this problem it is proposed to attempt to predict the motion trajectory of humans and obstacles. Two kinds of prediction are considered: short-term and long-term. The short-term prediction r...

In this paper, we deal with the short term prediction of the domestic electrical appliances power consumption. We go further than the prediction of on/off activities of appliances to the prediction of a bounded interval of consumed power. The short term prediction of appliances consumptions is an unavoidable problem to solve when constructing optimal schedules of appliance consumptions. For rel...

This paper investigates the long-term prediction of MPEG video traffic. Predicting such traffic over a long horizon is important for today’s fast networks and internet multimedia services. In comparison with short-term prediction, long-term prediction of video traffic is yet to be explored especially for MPEG-4 coded videos despite its effectiveness in a number of important network-edge applica...

this paper employs a general non-linear analysis tool to analyse the nature of time series associated with the price (returns) of a particular company in tehran stock exchange. it is shown that the behavior of the process associated with the price (returns) time-series of this company is weakly chaotic, and due to the non-random behavior of the process, short term prediction of stock price is p...

Time series forecasting is of fundamental importance for a variety of domains including the prediction of earthquakes, financial market prediction, and the prediction of epileptic seizures. We present an original approach that brings a novel perspective to the field of long-term time series forecasting. Nonlinear properties of a time series are evaluated and used for long-term predictions. We u...

There exists a wide range of paradigms, and a high number of different methodologies that are applied to the problem of time series prediction. Most of them are presented as a modified function approximation problem using input/output data, in which the input data are expanded using values of the series at previous steps. Thus, the model obtained normally predicts the value of the series at a t...

Abstract Real‐time monitoring and accurate prediction of photovoltaic (PV) power generation operation parameters are essential to ensure stable operation. In this paper, a set online PV parameter measurement devices characterized by simple structure, high sampling accuracy, small data fluctuations, ease measurement, designed. Sensors based on the zero‐flux principle employed in real‐time collec...

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term pr...