Solar Power Generation Analysis and Forecasting Real-World Data Using LSTM and Autoregressive CNN

Tosun, Nail; Sert, Egemen; Ayaz, Enes; Yilmaz, Ekin; Göl, Murat

doi:10.1109/sest48500.2020.9203124

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…This model has been validated on data collected from 34 locations spread across three different climate zones, and the results suggest that the proposed model outperforms all reference models that employ CNN or LSTM. Other works that integrate CNN and RNN to forecast PV generation or meteorological parameters can be found in (Lee et al, 2018;Wang et al, 2019;Tosun et al, 2020;Suresh et al, 2020). However, the above models do not apply sky imaging system to investigate the impact of cloud cover.…”

Section: Ll Open Accessmentioning

confidence: 99%

Intra-hour irradiance forecasting techniques for solar power integration: A review

Chu¹,

Coimbra

et al. 2021

iScience

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The ever-growing installation of solar power systems imposes severe challenges on the operations of local and regional power grids due to the inherent intermittency and variability of ground-level solar irradiance. In recent decades, solar forecasting methodologies for intra-hour, intra-day and day-ahead energy markets have been extensively explored as cost-effective technologies to mitigate the negative effects on the power grids caused by solar power instability. In this work, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized. The theories behind the forecasting methodologies and how these theories are applied in various forecasting models are presented. The reviewed mathematical tools include regressive methods, stochastic learning methods, deep learning methods, and genetic algorithm. The reviewed forecasting methodologies include data-driven methods, local-sensing methods, hybrid forecasting methods, and application orientated methods that generate probabilistic forecasts and spatial forecasts. Furthermore, suggestions to accelerate the development of future intra-hour forecasting methods are provided.

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Section: Ll Open Accessmentioning

confidence: 99%

Intra-hour irradiance forecasting techniques for solar power integration: A review

Chu¹,

Coimbra

et al. 2021

iScience

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“…In the field of time series forecasting [ [6] , [7] , [8] ], various algorithms such as Auto-Regressive Integrated Moving Average (ARIMA), Prophet, Seasonal ARIMA (SARIMA), and LSTM have been employed to forecast irradiance. Among these algorithms, LSTM has shown significant promise due to its ability to forecast long-term and short-term data accurately.…”

Section: Introductionmentioning

confidence: 99%

Solar forecasting for a PV-battery powered DC system

et al. 2023

Heliyon

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Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States

Valipour

Khoshkam

Bateni

et al. 2023

Agricultural Water Management

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Solar Power Generation Analysis and Forecasting Real-World Data Using LSTM and Autoregressive CNN

Cited by 8 publications

References 15 publications

Intra-hour irradiance forecasting techniques for solar power integration: A review

Intra-hour irradiance forecasting techniques for solar power integration: A review

Solar forecasting for a PV-battery powered DC system

Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States

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