A review of deep learning for renewable energy forecasting

Energy Conversion and Management, Год журнала: 2019, Номер 198, С. 111799 - 111799

Опубликована: Июль 17, 2019

Язык: Английский

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A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization

Renewable and Sustainable Energy Reviews, Год журнала: 2020, Номер 124, С. 109792 - 109792

Опубликована: Март 2, 2020

Язык: Английский

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Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda

International Journal of Information Management, Год журнала: 2020, Номер 53, С. 102104 - 102104

Опубликована: Апрель 20, 2020

Язык: Английский

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Forecasting: theory and practice

International Journal of Forecasting, Год журнала: 2022, Номер 38(3), С. 705 - 871

Опубликована: Янв. 20, 2022

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds future is both exciting challenging, with individuals organisations seeking to minimise risks maximise utilities. large number forecasting applications calls for a diverse set methods tackle real-life challenges. This article provides non-systematic review theory practice forecasting. We provide an overview wide range theoretical, state-of-the-art models, methods, principles, approaches prepare, produce, organise, evaluate forecasts. then demonstrate how such theoretical concepts are applied in variety contexts. do not claim this exhaustive list applications. However, we wish our encyclopedic presentation will offer point reference rich work undertaken over last decades, some key insights practice. Given its nature, intended mode reading non-linear. cross-references allow readers navigate through various topics. complement covered by lists free or open-source software implementations publicly-available databases.

Язык: Английский

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A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework

Energy Conversion and Management, Год журнала: 2020, Номер 212, С. 112766 - 112766

Опубликована: Апрель 10, 2020

Язык: Английский

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Prediction of solar energy guided by pearson correlation using machine learning

Energy, Год журнала: 2021, Номер 224, С. 120109 - 120109

Опубликована: Фев. 19, 2021

Язык: Английский

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Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

IET Renewable Power Generation, Год журнала: 2019, Номер 13(7), С. 1009 - 1023

Опубликована: Фев. 7, 2019

The modernisation of the world has significantly reduced prime sources energy such as coal, diesel and gas. Thus, alternative based on renewable have been a major focus nowadays to meet world's demand at same time reduce global warming. Among these sources, solar is source that used generate electricity through photovoltaic (PV) system. However, performance power generated highly sensitive climate seasonal factors. unpredictable behaviour affects output causes an unfavourable impact stability, reliability operation grid. Thus accurate forecasting PV crucial requirement ensure stability This study provides systematic critical review methods forecast with main metaheuristic machine learning methods. Advantages disadvantages each method are summarised, historical data along horizons input parameters. Finally, comprehensive comparison between compiled assist researchers in choosing best technique for future research.

Язык: Английский

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Photovoltaic power forecasting based LSTM-Convolutional Network

Energy, Год журнала: 2019, Номер 189, С. 116225 - 116225

Опубликована: Сен. 26, 2019

Язык: Английский

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Extensive comparison of physical models for photovoltaic power forecasting

Applied Energy, Год журнала: 2020, Номер 283, С. 116239 - 116239

Опубликована: Дек. 4, 2020

Forecasting the power production of grid-connected photovoltaic (PV) plants is essential for both profitability and prospects technology. Physically inspired modelling represents a common approach in calculating expected output from numerical weather prediction data. The model selection has high effect on physical PV forecasting accuracy, as difference between most least accurate chains 13% mean absolute error (MAE), 12% root square (RMSE), 23–33% skill scores plant average. forecast performance analysis performed verified one-year 15-min resolution data 16 Hungary day-ahead intraday time horizons all possible combinations nine direct diffuse irradiance separation, ten tilted transposition, three reflection loss, five cell temperature, four module performance, two shading inverter models. critical calculation steps are identified separation transposition modelling, while models important. Absolute squared errors conflicting metrics, more detailed result lowest MAE, simplest ones have RMSE. Wind speed forecasts only marginal prediction. results this study contribute to deeper understanding research community, main conclusions also beneficial owners preparing their generation forecasts.

Язык: Английский

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Taxonomy research of artificial intelligence for deterministic solar power forecasting

Energy Conversion and Management, Год журнала: 2020, Номер 214, С. 112909 - 112909

Опубликована: Май 1, 2020

Язык: Английский

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