Power forecasting for a photovoltaic system based on the multi-agent adaptive fuzzy neuronet

9th International Multidisciplinary Scientific and Research Conference "Modern Issues in Science and Technology" Workshop "Advanced Technologies in Aerospace, Mechanical and Automation Engineering"

自然科学;工业技术

Degtyarev, Alexander S.^1 ; Kosenko, Viktor I.^2 ; Engel, Ekaterina A.^3 ; Engel, Nikita N.^3 ; Savelyeva, Marina V.^4

JSC Central Construction Bureau Geofizika, 89 Kirenskogo street, Krasnoyarsk, Russia^1

JSC Academician M F Reshetnev Information Satellite Systems, 52 Lenin street, Zheleznogorsk, Krasnoyarsk region

662972, Russia^2

Katanov Khakass State University, 92, Lenina ave., Abakan

655017, Russia^3

Reshetnev Siberian State University of Science and Technology, 31, Krasnoyarsky Rabochy Av., Krasnoyarsk

660037, Russia^4

关键词: Adaptive fuzzy;    Levenberg-Marquardt algorithm;    Multi agent;    Photovoltaic systems;    Power forecasting;    Random perturbations;    Recurrent networks;    Residential building;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/450/7/072012/pdf
DOI  :  10.1088/1757-899X/450/7/072012

来源: IOP

【 摘 要 】

This article presents a multi-agent adaptive fuzzy neuronet for a two days ahead forecasting of the hourly power from a photovoltaic system under random perturbations. In this research we consider a 5 KW Solar Power Plant for a residential building (model SA-5000M). The main objective of this research is to fulfil the multi-agent adaptive fuzzy neurone for hourly power forecasting for a photovoltaic system. The agents of the multi-agent adaptive fuzzy neuronet are fulfilled as two-layered recurrent networks. The standard Levenberg-Marquardt algorithm is described. The analysis of the evolving errors shows the potential of the multi-agent adaptive fuzzy neuronet in the hourly power forecasting for a photovoltaic system.