Short-term solar PV forecasting based on recurrent neural network and clustering

Ouyang, Wen; Yu, Kun-Ming; Sodsong, Nattawat; Chuang, Ken H.

doi:10.1117/12.2550322

Cited by 5 publications

(2 citation statements)

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“…In PV power production, [38] Designed a cascade architecture consisting of RNN models that utilized hierarchical clustered data to tackle PV power fluctuations and designed an accurate model. [39] experimented with 3 RNN layers to predict 5 min, 15 min, 1 h, and 3 h ahead, with an input sampling of 12 time-steps.…”

Section: Problem Definition and Deep Learning Modelsmentioning

confidence: 99%

Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies

Husein,

Gago,

Hasan

et al. 2024

Heliyon

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Section: Problem Definition and Deep Learning Modelsmentioning

confidence: 99%

Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies

Husein,

Gago,

Hasan

et al. 2024

Heliyon

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“…For example, Ouyang et al [59] proposed an RNN-based PV power generation forecasting model, which was combined with clustering algorithms. The model exhibited good forecasting results in sunny days.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting

et al. 2020

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Accurately forecasting power generation in photovoltaic (PV) installations is a challenging task, due to the volatile and highly intermittent nature of solar-based renewable energy sources. In recent years, several PV power generation forecasting models have been proposed in the relevant literature. However, there is no consensus regarding which models perform better in which cases. Moreover, literature lacks of works presenting detailed experimental evaluations of different types of models on the same data and forecasting conditions. This paper attempts to fill in this gap by presenting a comprehensive benchmarking framework for several analytical, data-based and hybrid models for multi-step short-term PV power generation forecasting. All models were evaluated on the same real PV power generation data, gathered from the realisation of a small scale pilot site in Thessaloniki, Greece. The models predicted PV power generation on multiple horizons, namely for 15 min, 30 min, 60 min, 120 min and 180 min ahead of time. Based on the analysis of the experimental results we identify the cases, in which specific models (or types of models) perform better compared to others, and explain the rationale behind those model performances.

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Stochastic model for prediction of microgrid photovoltaic power generation

Baeva¹,

Stanev²,

Popov³

et al. 2021

Thermophysical Basis of Energy Technologies (Tbet 2020)

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Short-term solar PV forecasting based on recurrent neural network and clustering

Cited by 5 publications

References 0 publications

Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies

Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies

Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting

Stochastic model for prediction of microgrid photovoltaic power generation

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