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

Fei Wang,Zhao Zhen,Bo Wang,Zengqiang Mi

doi:10.3390/app8010028

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.

Highlights

By 2020, the energy consumption within developing countries is expected to double from 2015 levels [1]
A comprehensive comparison between support vector machines (SVM) and K-nearest neighbors (KNN) in different situations is conducted to achieve a basic understanding of their classification performance
The factors influencing classification accuracy for the KNN method considered in this paper are: the quantity of training data and the value of parameter K

Summary

Introduction

By 2020, the energy consumption within developing countries is expected to double from 2015 levels [1]. This additional generation capacity, especially if based on non-renewable resources, will have negative consequences to Earth’s climate, which in turn adds urgency to the development and integration of renewable energy technologies. Proposed a hi-Ren Scenario with the slower deployment of nuclear, and carbon capture and storage technologies, but the more rapid deployment of renewables, notably solar and wind energy, to prevent increasing levels of CO2 emissions [2]. The growth of renewables is aggressive in China, which invested $83.3 billion in renewable energy in 2014, and leads other countries in this area.

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Conclusion