A hybrid method for day‐ahead photovoltaic power forecasting based on generative adversarial network combined with convolutional autoencoder

Pan, Xin; Zhou, Jieyang; Sun, Xiaorong; Cao, Yang; Xiao-mei, Cheng; Farahmand, Hossein

doi:10.1049/rpg2.12619

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…Solar power has a high space and time dependence. Over the past few years, more and more researchers have focused on applying a spatiotemporal perspective into developed forecasting models in order to improve their accuracy [100]. Aiming to facilitate the control and reduce the operation cost of power systems, implementation of spatiotemporal information into forecasting models could further enhance this process [101].…”

Section: Discussion and Future Researchmentioning

confidence: 99%

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

Bazionis,

Kousounadis‐Knousen,

Georgilakis

et al. 2023

IET Renewable Power Gen

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A review of the state‐of‐the‐art in short‐term Solar Power Forecasting (SPF) methodologies is presented in this paper. Over the last few years, developing and improving solar forecasting models has been the main focus of researchers, considering the need to efficiently increase their forecasting accuracy. Forecasting models aim to be used as an efficient tool to help with the stability and control of energy systems and electricity markets. Intending to further comprehend the factors affecting the quality of SPF models, this paper focuses on short‐term solar forecasting methodologies since they pose a crucial role in the daily operation and scheduling of power systems, since they focus on forecasting horizons typically ranging from 1 h to 1 day. The reviewed works are classified according to the climatic conditions, technical characteristics, and the forecasting errors of the different methodologies, providing readers with information over various different cases of SPF. Considering the need to improve the SPF efficiency, such classifications allow for important comparative conclusions to be drawn, depending on the location of each case and the meteorological data available. Future directions in the field of short‐term solar power forecasting are proposed considering the increasing development of SPF models’ architecture and their field of focus.

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Section: Discussion and Future Researchmentioning

confidence: 99%

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

Bazionis,

Kousounadis‐Knousen,

Georgilakis

et al. 2023

IET Renewable Power Gen

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“…LSTM autoencoder model used for day ahead forecast for PV power generation is introduced in [11] and autoencoder but with GRU network is also used for short-term PV power generation forecasting in [12]. A convolutional autoencoder combined generative adversarial network is proposed to be used for day ahead PV power forecasting [13].…”

Section: Fig 1 Lstm Blockmentioning

confidence: 99%

“…Fig 13. The actual and predicted results for the 1 h ahead forecasting to PV power generation data using the comparative models with PV power generation and weather data: (a) LSTM model, (b) CNN-LSTM…”

mentioning

confidence: 99%

A hybrid model of CNN and LSTM autoencoder-based short-term PV power generation forecasting

Ibrahim,

Gharghory,

Kamal

2024

Electr Eng

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Solar energy is one of the main renewable energies available to fulfill global clean energy targets. The main issue of solar energy like other renewable energies is its randomness and intermittency which affects power grids stability. As a solution for this issue, energy storage units could be used to store surplus energy and reuse it during low solar generation intervals. Also, in order to sustain stable power grid and better grid operation and energy storage management, photovoltaic (PV) power forecasting is inevitable. In this paper, new hybrid model based on deep learning techniques is proposed to predict short-term PV power generation. The proposed model incorporates convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder network. The new model differentiates itself in accomplishing high prediction accuracy by extracting spatial features in time series via CNN layers and temporal features between the time series data through LSTM. The introduced model is tested on dataset of power generation from southern UK solar farm and the weather data corresponding to same location and time intervals; the forecasting performance of the suggested model is evaluated in metrics of root-mean-square error (RMSE) and mean absolute error (MAE). The used model is compared with different models from the literature either of pure type of network such as LSTM and gated recurrent unit (GRU) or hybrid combination of different networks like CNN-LSTM and CNN-GRU. The results show that proposed model provides enhanced results and reduces training time significantly compared to other competitive models, where the performance of the proposed model improved averagely by 5% to 25% in terms of RMSE and MAE performance metrics, and the execution time of training significantly reduced with almost 70% less compared to other models.

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“…This approach primarily relies on extensive historical output data and meteorological data to establish a mapping relationship between the conditions governing photovoltaic power prediction and the desired output volume using artificial intelligence algorithms [11,12]. Common machine learning prediction methods encompass back propagation (BP) neural networks and recurrent neural networks (RNN) [13][14][15][16][17]. While these neural network models have notably augmented the prediction accuracy of photovoltaic power output for photovoltaic power stations, the initial configuration parameters and weights of individual computational models are randomly assigned.…”

Section: Introductionmentioning

confidence: 99%

Short‐term power forecasting method for 5G photovoltaic base stations on non‐sunny days based on SDN‐integrated INGO‐BP and RGAN

Huang,

Guo,

Wei

et al. 2024

IET Renewable Power Gen

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In response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement weather conditions, this research article introduces a concise and effective method for short‐term power prediction of PVBSs when subjected to non‐sunny weather conditions, leveraging the paradigm of software defined networking. This method ingeniously amalgamates the improved Northern Goshawk optimized back propagation neural network (INGO‐BP) and recurrent generative adversarial networks (RGAN). The investigation commences by delineating a comprehensive PVBS system framework underpinned by SDN principles. Subsequently, it employs the Pearson correlation coefficient technique and principal component analysis for the judicious selection of pertinent features from the PVBS photovoltaic dataset, thereby ameliorating the issue of multicollinearity within the predictive model. To culminate the research endeavour, the INGO algorithm is enlisted to optimize the weightings and bias parameters of the BP neural network, while RGAN is harnessed for the acquisition of distinctive distribution patterns in photovoltaic data across diverse meteorological conditions. This augmentation enhances the model's capacity to discern the intricate long‐term mappings between photovoltaic power and meteorological data. The empirical findings substantiate the elevated accuracy of the INGO‐BP prediction model across sunny, cloudy, and rainy weather conditions, with a discernible enhancement in predictive accuracy for PVBS performance under non‐sunny weather conditions facilitated by data augmentation via RGAN.

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A hybrid method for day‐ahead photovoltaic power forecasting based on generative adversarial network combined with convolutional autoencoder

Cited by 8 publications

References 38 publications

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

A hybrid model of CNN and LSTM autoencoder-based short-term PV power generation forecasting

Short‐term power forecasting method for 5G photovoltaic base stations on non‐sunny days based on SDN‐integrated INGO‐BP and RGAN

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