Review of photovoltaic power forecasting

Solar Energy, Journal Year: 2016, Volume and Issue: 136, P. 78 - 111

Published: July 8, 2016

Language: Английский

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Forecasting of photovoltaic power generation and model optimization: A review

Renewable and Sustainable Energy Reviews, Journal Year: 2017, Volume and Issue: 81, P. 912 - 928

Published: Sept. 1, 2017

Language: 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, Journal Year: 2020, Volume and Issue: 124, P. 109792 - 109792

Published: March 2, 2020

Language: Английский

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Solar photovoltaic generation forecasting methods: A review

Energy Conversion and Management, Journal Year: 2017, Volume and Issue: 156, P. 459 - 497

Published: Dec. 1, 2017

Language: Английский

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On recent advances in PV output power forecast

Solar Energy, Journal Year: 2016, Volume and Issue: 136, P. 125 - 144

Published: July 8, 2016

Language: Английский

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Prediction of solar energy guided by pearson correlation using machine learning

Energy, Journal Year: 2021, Volume and Issue: 224, P. 120109 - 120109

Published: Feb. 19, 2021

Language: Английский

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Artificial neural network‐based photovoltaic maximum power point tracking techniques: a survey

IET Renewable Power Generation, Journal Year: 2015, Volume and Issue: 9(8), P. 1043 - 1063

Published: July 27, 2015

Recent researches oriented to photovoltaic (PV) systems feature booming interest in current decade. For efficiency improvement, maximum power point tracking (MPPT) of PV array output is mandatory. Although classical MPPT techniques offer simplified structure and implementation, their performance degraded when compared with artificial intelligence‐based especially during partial shading rapidly changing environmental conditions. Artificial neural network (ANN) algorithms several capabilities such as: (i) off‐line training, (ii) nonlinear mapping, (iii) high‐speed response, (iv) robust operation, (v) less computational effort (vi) compact solution for multiple‐variable problems. Hence, ANN have been widely applied as techniques. Among various available ANN‐based techniques, very limited references gather those a survey. Neither classification nor comparisons between competitors exist. Moreover, no detailed analysis the system under has previously discussed. This study presents survey based The authors propose new categorisation on controller input variables. In addition, comparison from points view, structure, experimental verification transient/steady‐state presented. are taken into consideration update purpose.

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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Distribution Voltage Regulation Through Active Power Curtailment With PV Inverters and Solar Generation Forecasts

IEEE Transactions on Sustainable Energy, Journal Year: 2016, Volume and Issue: 8(1), P. 13 - 22

Published: June 7, 2016

Distribution voltage profiles are subjected to overvoltage limit violations from high penetration of grid-connected photovoltaic (PV) systems. Such rises seen at the point PV interconnection can be mitigated by adaptively changing active and/or reactive power injection inverter. This work proposes a local regulation technique that utilizes very short-term (15 s) forecasts circumvent imminent upper violation or an scenario. To provide these generation forecasts, hybrid forecasting method is formulated based on Kalman filter theory, which applies physical modeling using high-resolution data on-site measurements. The proposed algorithm employs curtailment when estimate given droop-based cannot desired within predefined factor limits. threshold values calculated in such way this reduce possible violations. effectiveness demonstrated with case studies developed standard test feeder realistic load and profiles.

Language: Английский

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Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

Energies, Journal Year: 2019, Volume and Issue: 12(9), P. 1621 - 1621

Published: April 29, 2019

We compare the 24-hour ahead forecasting performance of two methods commonly used for prediction power output photovoltaic systems. Both are based on Artificial Neural Networks (ANN), which have been trained same dataset, thus enabling a much-needed homogeneous comparison currently lacking in available literature. The dataset consists an hourly series simultaneous climatic and PV system parameters covering entire year, has clustered to distinguish sunny from cloudy days separately train ANN. One method feeds only while other is hybrid as it relies upon daily weather forecast. For days, first shows very good stable performance, with almost constant Normalized Mean Absolute Error, NMAE%, all cases (1% < NMAE% 2%); even better (NMAE% 1%) considered this analysis, but overall less > 2% up 5.3% cases). both typically drops; rather that does not use forecasts, varies significantly analysis.

Language: Английский

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