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Currently, many of the elements that surround us in daily life need software systems that work from the information available in the domain (data-driven application domains) by performing a process of data mining from it. Between the data mining techniques used in everyday problems we find the k-Nearest Neighbors technique. However, in domains and real situations it is very common to find vague, ambiguous...
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...
As a non-contact-type device could sample part surface data with high speed and accuracy, it becomes the most popular instrument for capturing the surface data of a part. However, it creates a large amount of point data which must be reduced to decrease computational time and to lower the storage requirement. Aiming at the limitations of point cloud data reduction methods developed in the past, a...
Simplification of scattered point cloud is one of the key preprocessing technologies in reverse engineering. Most simplification algorithms always lose geometric feature excessively in the process. On the basis of feature extraction, a new algorithm is proposed for the simplification of scattered point cloud with unit normal vectors. First, points in point cloud are distributed into uniform cubes...
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