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

Renewable and Sustainable Energy Reviews, Journal Year: 2020, Volume and Issue: 124, P. 109792 - 109792

Published: March 2, 2020

Language: Английский

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Forecasting: theory and practice

International Journal of Forecasting, Journal Year: 2022, Volume and Issue: 38(3), P. 705 - 871

Published: Jan. 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.

Language: Английский

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Potential of building integrated and attached/applied photovoltaic (BIPV/BAPV) for adaptive less energy-hungry building’s skin: A comprehensive review

Journal of Cleaner Production, Journal Year: 2020, Volume and Issue: 276, P. 123343 - 123343

Published: Aug. 9, 2020

Language: Английский

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Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Applied Sciences, Journal Year: 2020, Volume and Issue: 10(2), P. 487 - 487

Published: Jan. 9, 2020

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate forecasters remains challenging issue, particularly multistep-ahead prediction. Accurate PV forecasting critical in number applications, such as micro-grids (MGs), energy optimization and management, integrated smart buildings, electrical vehicle chartering. Over last decade, vast literature has been produced on this topic, investigating numerical probabilistic methods, physical models, artificial intelligence (AI) techniques. This paper aims at providing complete review recent applications AI techniques; we will focus machine learning (ML), deep (DL), hybrid these branches are becoming increasingly attractive. Special attention be paid to development application DL, well future trends topic.

Language: Английский

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CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production

Electric Power Systems Research, Journal Year: 2022, Volume and Issue: 208, P. 107908 - 107908

Published: March 12, 2022

Language: Английский

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Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting

IEEE Access, Journal Year: 2019, Volume and Issue: 7, P. 74822 - 74834

Published: Jan. 1, 2019

With the fast expansion of renewable energy system installed capacity in recent years, availability, stability, and quality smart grids have become increasingly important. The output forecasting applications also been developing rapidly such techniques particularly applied fields wind solar photovoltaic (PV). In case PV forecasting, many performed with machine learning hybrid techniques. this paper, we propose a high-precision deep neural network model named PVPNet to forecast power. methodology behind proposed is based on networks, able generate 24-h probabilistic deterministic power meteorological information, as temperature, radiation, historical data. accuracy determined by mean absolute error (MAE) root square (RMSE) values. results from experiments show that MAE RMSE algorithm are 109.4845 163.1513, respectively. prove prediction outperforms other benchmark models, effectively predicts complex time series high degree volatility irregularity.

Language: Английский

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Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization

Journal of Cleaner Production, Journal Year: 2020, Volume and Issue: 277, P. 123948 - 123948

Published: Aug. 29, 2020

Language: Английский

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Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models

Renewable Energy, Journal Year: 2021, Volume and Issue: 177, P. 101 - 112

Published: May 22, 2021

Language: Английский

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Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

Applied Energy, Journal Year: 2020, Volume and Issue: 268, P. 115023 - 115023

Published: April 21, 2020

Language: Английский

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Artificial intelligence techniques for enabling Big Data services in distribution networks: A review

Renewable and Sustainable Energy Reviews, Journal Year: 2021, Volume and Issue: 150, P. 111459 - 111459

Published: July 16, 2021

Artificial intelligence techniques lead to data-driven energy services in distribution power systems by extracting value from the data generated deployed metering and sensing devices. This paper performs a holistic analysis of artificial applications networks, ranging operation, monitoring maintenance planning. The potential for system needed sources are identified classified. following networks analyzed: topology estimation, observability, fraud detection, predictive maintenance, non-technical losses forecasting, management systems, aggregated flexibility trading. A review methods implemented each these is conducted. Their interdependencies mapped, proving that multiple can be offered as single clustered service different stakeholders. Furthermore, dependencies between AI with identified. In recent years there has been significant rise deep learning time series prediction tasks. Another finding unsupervised mainly being applied customer segmentation, buildings efficiency clustering consumption profile grouping detection. Reinforcement widely design, although more testing real environments needed. Distribution network sensorization should enhanced increased order obtain larger amounts valuable data, enabling better outcomes. Finally, future opportunities challenges applying grids discussed.

Language: Английский

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