Photovoltaic Power Regression Model Based on Gauss Boltzmann Machine

Lu, Zhengding; Wang, Zehan; Ren, Yimo

doi:10.23919/ccc50068.2020.9189246

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Pv Uncertainty1

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2023

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“…It always considers the most probable daily, monthly, or annual prediction [26]. Different probability destiny functions PDF are proposed to predict PV such as parametric [27][28], and non-parametric [29][30][31][32]. Long-term uncertainty uses historical measured data for several past years.…”

Section: Pv Uncertaintymentioning

confidence: 99%

Assessing Photovoltaic Power Uncertainty in Low-income Smart Home Energy Management using Geographic Information Systems Data

El-Azab

Saif

Metwally

et al. 2023

Preprint

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Solar-powered homes can be an optimal solution for the lack of continuous power sources problem in initial low-income communities. However, the challenge of PV uncertainty can make it difficult to coordinate this vital solar energy in real-time. This paper proposes a new, low-cost solution for assessing the uncertainty of photovoltaic power generation in smart home energy management systems. The proposed index, inspired by the well-known clearness index, is an adaptive deterministic indicator that only requires free Geographic Information System GIS models and PV power measurement, without the need for expensive high-tech controllers or expert engineers/programmers. The proposed index successfully predicts the daily PV energy with errors of less than 3% for more than 93% of studied days, according to the 2020 measured solar radiation of the studied case.

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