Regional photovoltaic grid power generation output prediction based on SVM and autoencoder

Abstract

Photovoltaic power generation, which is an important component of clean energy, has been widely used around the world. However, the predictability of photovoltaic power generation systems has always been a challenge because it is affected by meteorological conditions and other external factors. Therefore, accurate photovoltaic power generation output prediction is crucial for the stability and reliability of the power system. This work aims to use two advanced machine learning technologies, supporting vector machine (SVM), and autoencoder (Autoencoder), to improve the prediction performance of regional photovoltaic grid power generation output. First, this article introduces the autoencoder and SVM network, and then we use the SVM algorithm to analyze and model these data. By using SVM, we can build a highly accurate photovoltaic power generation output prediction model that can predict on different time scales, from hourly level to daily level. Next, the principle and working mechanism of the support vector machine will be introduced in detail, and its advantages in processing complex data and high-dimensional features will be explained. Then, this article will use the grid search method to optimize the support vector machine model. A new cluster performance index based on electrical distance and regional voltage regulation capability is proposed, which divides the distribution network into multiple clusters. The voltage control strategy combines cluster autonomous optimization of time scales and distributed inter-cluster coordination optimization. Then we select evaluation indicators to evaluate the obtained model. The results show that the model can predict power generation output and provide a reference for making point arrangements. In terms of model building, this article will explain how to select appropriate features and parameters and use the collected data to train the SVM model. We also perform a detailed evaluation and analysis of the model’s performance. We use a variety of evaluation metrics to measure model accuracy and stability. Experimental results show that the SVM and autoencoder hybrid model have higher prediction accuracy and better generalization performance than traditional methods.