Time series prediction with neural networks has been the focus of much research in past few decades. Given recent deep learning revolution, there attention using models for time prediction, and hence it is important to evaluate their strengths weaknesses. In this paper, we present an evaluation study that compares performance multi-step ahead prediction. The methods comprise simple recurrent ne...

Due to the rapid increase in deployment and high penetration of solar power generation worldwide, solar power generation forecasting has become critical to variable generation integration planning, and within utility and independent system operator (ISO) operations. Utilities and ISOs require day ahead and hour ahead as well as intra-hour solar power forecasts for core operations solar power pr...

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...

Differential base station sometimes is not capable of sending correction information for minutes, due to radio interference or loss of signals. To overcome the degradation caused by the loss of Differential Global Positioning System (DGPS) Pseudo-Range Correction (PRC), predictions of PRC is possible. In this paper, the Support Vector Machine (SVM) and Genetic Algorithms (GAs) will be incorpor...

The fast adoption of electric vehicles (EVs) has resulted in a growing concern for the planning and operation distribution power system. Thus, it is crucial to comprehensively assess influence EV charging load on To mitigate operational risks optimize control measures network, two-stage time series decomposition BiLSTM deep learning architecture proposed accurately predicting series. In first s...

It is necessary to form an energy system in order to deliver the electrical power to a region that delivering energy to them from the national grid is not possible. At the moment, using the hybrid renewable energy systems (HRES) is the suggestion of more articles and scientific books because of environmental problems, nearing to the end of fossil fuels, and challenges including unpredictability...

This paper proposes a novel non-parametric approach for the analysis and prediction of electricity price curves by applying the manifold learning methodology. Cluster analysis based on the embedded low-dimensional manifold of the original price data is employed to identify characteristics of the price curve shape. The proposed price curve model performs well in forecasting both short-term price...

Changes in prices of metals in London Metal Exchange during the last ten years are analyzed, to check possibilities of their forecasting in medium-term horizon (from 1 month to 1 year ahead). The predictions based on extrapolation of linear trends and cyclic components are proposed. It is shown that a cycle period can be selected by analysis of rolling Fourier spectra of the series. The predict...

h A YEAR AGO, this column contemplated the road ahead for semiconductor-based products that build on basic design, test, process, and device technologies. In late 2011, a workshop on the ‘‘Future of IT and Society’’ presented several broad visions of how societal changes will build on semiconductor-based products and information technology in such contexts as social mobility, healthcare, securi...