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The increasing penetration of stochastic photovoltaic (PV) generation in electric power systems poses significant challenges to system operators. To ensure reliable operation of power systems, accurate forecasting of PV power production is essential. In this paper, we propose a novel multitime-scale data-driven forecast model to improve the accuracy of short-term PV power production. This model leverages...
This paper proposes a radial basis function (RBF) neural network-based model for short-term solar power prediction (SPP). Instead of predicting solar power directly, the model predicts transmissivity, which is then used to obtain solar power according to the extraterrestrial radiation. The proposed model uses a novel two-dimensional (2D) representation for hourly solar radiation and uses historical...
Traffic prediction constitutes a new hot research topic of network metrology. Thus, tuning the prediction model parameters is very crucial to achieve accurate prediction. This work focuses on the design, the empirical evaluation and the analysis of the behavior of training-based models for predicting the throughput of a single link i.e. the incoming input data in Megabit per time interval called granularity...
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