Deep learning and wavelet transform integrated approach for short-term solar PV power prediction

Mishra, Manohar; Dash, Pandit Byomakesha; Nayak, Janmenjoy; Naik, Bighnaraj; Swain, Subrat Kumar

doi:10.1016/j.measurement.2020.108250

Cited by 133 publications

(46 citation statements)

References 49 publications

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“…The experimental results show that the LSTM–RNN model with the time correlation modification method achieves more accurate prediction results than the traditional LSTM–RNN. Mishra et al [57], developed a novel LSTM model with WT (LSTM‐WT) for short‐term solar prediction. The model is tested on the real‐world data obtained from the meteorological station, Urbana Champaign, Illinois and show higher prediction performance than the traditional regression methods and individual LSTM model.…”

Section: Analysis and Resultsmentioning

confidence: 99%

Systematic literature review of photovoltaic output power forecasting

Başaran

Bozyiğit²,

Siano

et al. 2020

IET Renewable Power Generation

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Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world‐wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.

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Section: Analysis and Resultsmentioning

confidence: 99%

Systematic literature review of photovoltaic output power forecasting

Başaran

Bozyiğit²,

Siano

et al. 2020

IET Renewable Power Generation

View full text Add to dashboard Cite

show abstract

“…To solve this problem, different methods can be used. For instance, Mishra et al [12] proposed a PV power generation prediction model based on the wavelet transform and LSTM-dropout network. This model uses wavelet transform to preprocess data of temperature, visibility, and cloudiness and a deep learning network model to accomplish the PV power generation prediction.…”

Section: Related Researchmentioning

confidence: 99%

Deep-Learning-Based Defect Evaluation of Mono-Like Cast Silicon Wafers

2021

Photonics

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Solar cells based on mono-like cast silicon (MLC-Si) have been attracting increasing attention in the photovoltaic (PV) market due to their high energy conversion efficiency and low cost. As in the production of monocrystalline silicon (MC-Si) and polycrystalline silicon (PC-Si) cells, various defects will inevitably occur during the production process of MLC-Si cells. Although computer vision technology has been employed for defect detection in the production processes, it is still difficult to achieve high accuracy in detecting defects in PV cells using traditional machine vision methods due to defect similarity and complex background. To address this challenge, a deep-learning-based quality assessment algorithm of MLC-Si wafers is proposed. Focusing on the dislocation defects, four different deep learning models are used to conduct migration learning and selected different optimizers (ADAM and SGDM) are used to optimize the network models, achieving good results in evaluating and comparing the quality of ML-Si wafers. On this basis, an improved network model MVGG-19 based on the VGG-19 is designed to improve the prediction accuracy further. The experimental results show that the prediction error of the improved network model is reduced by 63% (compared with VGG-19) and the reasoning speed reaches 10.22 FPS, indicating good detection performance.

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“…The WT along with LSTM was utilized by Wang F et al and the results proved that the models accuracy can be improved by WT (Wang et al 2018 ). The WT and LSTM network was also used by M. Mishra et al The study predicted the solar power for 1-h to 1-day ahead of time horizon in which WT was used to decompose the input raw data series into different frequency components and LSTM was used to forecast solar power (Mishra et al 2020 ). In the same line, the GRU network was proposed by B. Gao et al to forecast the day ahead solar irradiance.…”

Section: Introductionmentioning

confidence: 99%