An Approach Using Transformer-based Model for Short-term PV generation forecasting

Phan, Quoc-Thang; Wu, Yuan‐Kang; Dzung, Phan Quoc

doi:10.1109/icasi55125.2022.9774491

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Transformer is a neural network model based on self attention mechanism that can capture long-distance dependencies and achieve efficient parallel computing. In terms of forecasting PV [106] used the Transformer model for ultra short term power forecasting problems and select real PV power generation data from Hebei province for experiments, comparing the Transformer with DNN.…”

Section: Single Methodsmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Hu,

Lim,

Tian

et al. 2024

CAAI Artificial Intelligence Research

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Integrating artificial intelligence (AI) into photovoltaic (PV) systems has become a revolutionary approach to improving the efficiency, reliability, and predictability of solar power generation. In this paper, we explore the impact of AI technology on PV power generation systems and its applications from a global perspective. Central to the discussion are the pivotal applications of AI in maximum power point tracking (MPPT), power forecasting, and fault detection within the PV system. On the one hand, the integration with AI technology enables the optimization and improvement of the operational efficiency of PV systems. On the other hand, new challenges have been observed, mainly in the areas of data processing and model management. Moreover, advances in AI technology and hardware upgrades will lead to the rapid global popularization of new energy sources such as solar energy, which is expected to replace traditional energy sources. Finally, we describe forward-looking solutions including transfer learning, few-shot learning, and edge computing, as well as the state of the art.

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Section: Single Methodsmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Hu,

Lim,

Tian

et al. 2024

CAAI Artificial Intelligence Research

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“…As the field advances, cutting-edge research introduces even more advanced DL models for solar energy forecasting, leveraging complex architectures for increased accuracy. Phan et al [22] proposed a transformer deep learning model for one-hour-ahead PV power generation forecasting, leveraging two years of numerical weather prediction (NWP) data from Taiwan's Central Weather Bureau and PV output from North Taiwan sites. This model notably surpasses traditional ANN, LSTM, and GRU models in accuracy metrics like nRMSE and normalized mean absolute percentage error (nMAPE), highlighting its effectiveness amidst the challenges posed by solar energy's dependency on fluctuating weather conditions and day/night cycles.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Boucetta,

Amrane,

Chouder

et al. 2024

Energies

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Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged to manage the power grid. EMS optimizes electric grid operations through advanced metering, automation, and communication technologies. A critical component of EMS is power forecasting, which facilitates precise energy grid scheduling. This research paper introduces a deep learning hybrid model employing convolutional neural network–long short-term memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting. The proposed method integrates the variational mode decomposition (VMD) algorithm with the CNN-LSTM model to predict PV power output from a solar farm in Boussada, Algeria, spanning 1 January 2019, to 31 December 2020. The performance of the developed model is benchmarked against other deep learning models across various time horizons (15, 30, and 60 min): variational mode decomposition–convolutional neural network (VMD-CNN), variational mode decomposition–long short-term memory (VMD-LSTM), and convolutional neural network–long short-term memory (CNN-LSTM), which provide a comprehensive evaluation. Our findings demonstrate that the developed model outperforms other methods, offering promising results in solar power forecasting. This research contributes to the primary goal of enhancing EMS by providing accurate solar energy forecasts.

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“…Lin et al employed the Spring DWT attention layer to measure the similarity of query-key pairs of sequences . Santos et al and Phan et al employed the transformer-based time series forecasting model to predict the PV power generation for each hour (López Santos et al, 2022;Phan et al, 2022). L'Heureux et al proposed a transformerbased architecture for load forecasting (L'Heureux et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Deep learning model-transformer based wind power forecasting approach

Huang

Yan

2023

Front. Energy Res.

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The uncertainty and fluctuation are the major challenges casted by the large penetration of wind power (WP). As one of the most important solutions for tackling these issues, accurate forecasting is able to enhance the wind energy consumption and improve the penetration rate of WP. In this paper, we propose a deep learning model-transformer based wind power forecasting (WPF) model. The transformer is a neural network architecture based on the attention mechanism, which is clearly different from other deep learning models such as CNN or RNN. The basic unit of the transformer network consists of residual structure, self-attention mechanism and feedforward network. The overall multilayer encoder to decoder structure enables the network to complete modeling of sequential data. By comparing the forecasting results with other four deep learning models, such as LSTM, the accuracy and efficiency of transformer have been validated. Furthermore, the migration learning experiments show that transformer can also provide good migration performance.

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An Approach Using Transformer-based Model for Short-term PV generation forecasting

Cited by 6 publications

References 9 publications

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Deep learning model-transformer based wind power forecasting approach

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