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With the increasing penetration of solar photovoltaic (PV) generation in the power system, the reliability of the distribution system and efficiency of PV systems have garnered increasing attention in recent years. Forecasting the PV output is one way to decrease the uncertainty of such power systems. In this study, we present a K-Nearest Neighbors algorithm based forecasting model, which can provide...
Accurate forecasting of solar power is needed for the successful integration of solar energy into the electricity grid. In this paper we consider the task of predicting the half-hourly solar photovoltaic power for the next day from previous solar power and weather data. We propose and evaluate several clustering based methods, that group the days based on the weather characteristics and then build...
This paper studies the problem of simplifying densely distributed point-sampled models. Many computer graphics applications call for vivid, full detail models. However, the level of detail necessary is more important than this need for fidelity in rendering system. So it is useful to obtain simple versions of complex models. We have developed a novel simplification algorithm which can preserve the...
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