Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Abstract

Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

“…It is shown that the MLC method yielded similar results to the SVM technique, better than the supervised k-NN, ANN, and RFC methods, which matches the geological map well in most of the lithological units in the study area, except for the quartz vein. However, although the supervised technique k-NN has been one of the foremost techniques for classification in many fields [41,58], the lithological classification using the k-NN method in the Shibanjing ophiolite complex shows somewhat lower consistency with the geological map, especially in quartz diorite, basic, and ultrabasic rock, ultramafic rock, and limestone. The classified results using the k-NN method were greatly affected by the terrain, making it difficult to differentiate alluvium deposits from other lithological units.…”

“…As a non-parametric algorithm, k-NN employs an instance-based leaning algorithm, or a "lazy learning," to find a group of K samples nearest to unknown samples [40]. K is a key parameter and plays a significant role in the performance of the k-NN classifier [40,41]. In this study, the optimal value of k was 5.…”

“…As proposed by previous research, there are several common kernel functions for the SVM method, such as linear, polynomial, radial basis function, and sigmoid [41].…”

Peer Review #1 of "Diatom identification including life cycle stages through morphological and texture descriptors (v0.1)"

“…Manuscript to be reviewed algorithm increases the dimensionality of the features space, so a non-linear classification problem can be solved linearly. The distance between the hyperplane and the training data is called the functional marging, which is used as a confidence interval of classification results (Wang et al, 2017).…”

A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System

“…As shown in Figure 2, at the SCADA data acquisition time, the SCADA and PMU measurements are combined in the robust estimation with pseudo-measurements, which can filter the bad data in measurements and prepare for the next state estimation. In the distribution system, the observability is guaranteed by pseudo-measurements which are mainly obtained from load forecasting based on AMI measurements [32,33]. Due to a large number of load buses in the distribution system, there is a certain error in the load forecasting, so that the obtained pseudo-measurements have a low precision.…”