Deep Learning-Based Image Regression for Short-Term Solar Irradiance Forecasting on the Edge

Author:

Papatheofanous Elissaios AlexiosORCID,Kalekis Vasileios,Venitourakis Georgios,Tziolos Filippos,Reisis Dionysios

Abstract

Photovoltaic (PV) power production is characterized by high variability due to short-term meteorological effects such as cloud movements. These effects have a significant impact on the incident solar irradiance in PV parks. In order to control PV park performance, researchers have focused on Computer Vision and Deep Learning approaches to perform short-term irradiance forecasting using sky images. Motivated by the task of improving PV park control, the current work introduces the Image Regression Module, which produces irradiance values from sky images using image processing methods and Convolutional Neural Networks (CNNs). With the objective of enhancing the performance of CNN models on the task of irradiance estimation and forecasting, we propose an image processing method based on sun localization. Our findings show that the proposed method can consistently improve the accuracy of irradiance values produced by all the CNN models of our study, reducing the Root Mean Square Error by up to 10.44 W/m2 for the MobileNetV2 model. These findings indicate that future applications which utilize CNNs for irradiance forecasting should identify the position of the sun in the image in order to produce more accurate irradiance values. Moreover, the integration of the proposed models on an edge-oriented Field-Programmable Gate Array (FPGA) towards a smart PV park for the real-time control of PV production emphasizes their advantages.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3