Interpretable temporal-spatial graph attention network for multi-site PV power forecasting

Simeunović, Jelena; Schubnel, Baptiste; Alet, Pierre-Jean; Carrillo, Rafael E.; Frossard, Pascal

doi:10.1016/j.apenergy.2022.120127

Cited by 34 publications

(4 citation statements)

References 23 publications

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“…CNNs can be highly effective for solar forecasting purposes. For example, Simeunović et al in [43] use a Graph CNN for forecasting the production of multiple PV power plants, where each plant represents a node of the graph. This type of CNN can effectively model spatial and temporal dependencies between different PV power plants, identifying common patterns across different locations through these dependencies.…”

Section: Neural Network (Deep Learning)mentioning

confidence: 99%

“…Some other possibly useful references can be the Goddard Earth Sciences Data and Information Services Center (GES-DISC) https://disc.gsfc.nasa.gov/ (last accessed on 18 September 2023), Meteotest https://meteotest.ch/en/ (last accessed on 18 September 2023) [43,47], Moderate Resolution Imaging Spectroradiometer (MODIS) of NASA's Terra and Aqua satellites https://modis.gsfc.nasa.gov/data/dataprod/mod01.php (last accessed on 18 September 2023) [11] and the Finnish Meteorological Institute's open-source datasets https://en.ilmatieteenlaitos.fi/open-data-sets-available (last accessed on 18 September 2023) [90].…”

Section: Sourcesmentioning

confidence: 99%

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Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Pandžić,

Capuder

2023

Energies

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Solar forecasting is becoming increasingly important due to the exponential growth in total global solar capacity each year. More photovoltaic (PV) penetration in the grid poses problems for grid stability due to the inherent intermittent and variable nature of PV power production. Therefore, forecasting of solar quantities becomes increasingly important to grid operators and market participants. This review presents the most recent relevant studies focusing on short-term forecasting of solar irradiance and PV power production. Recent research has increasingly turned to machine learning to address this challenge. The paper provides a discussion about building a solar forecasting model, including evaluation measures and machine learning method selection through analysed literature. Given that machine learning is data-driven, the focus of this review has been placed on data sources referenced in the literature. Open-access data sources have been compiled and explored. The main contribution of this paper is the establishment of a benchmark for assessing the performance of solar forecasting models. This benchmark utilizes the mentioned open-source datasets, offering a standardized platform for future research. It serves the crucial purpose of streamlining investigations and facilitating direct comparisons among different forecasting methodologies in the field of solar forecasting.

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Section: Neural Network (Deep Learning)mentioning

confidence: 99%

Section: Sourcesmentioning

confidence: 99%

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Pandžić,

Capuder

2023

Energies

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“…Reference [30] proposes a new two-stage deep learning method for photovoltaic power generation prediction, which has signifcant improvement and robustness in point prediction and probabilistic prediction tasks. In [31], a graph-based multisite daytime PV generation prediction model is presented. It is possible to interpret which PV stations and time steps infuence the prediction.…”

Section: Introductionmentioning

confidence: 99%

PV Power Forecasting in the Hexi Region of Gansu Province Based on AP Clustering and LSTNet

Li,

Yang,

Gou

2024

International Transactions on Electrical Energy Systems

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Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness.

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“…New advances in machine learning, for example, offer tools to better forecast renewable energy sources and demand given various weather input data. These approaches use weather data in different formats like single time series, e. g. (Dahl et al 2017;Hu et al 2021;Ren et al 2022;Elizabeth Michael et al 2022;Beichter et al 2022), to grid-based data, e. g. (Feng et al 2022;Kong et al 2020;Si et al 2021), or graphs, e. g. (Hu et al 2022;Simeunović et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Using weather data in energy time series forecasting: the benefit of input data transformations

Neumann,

Turowski,

Mikut

et al. 2023

Energy Inform

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Renewable energy systems depend on the weather, and weather information, thus, plays a crucial role in forecasting time series within such renewable energy systems. However, while weather data are commonly used to improve forecast accuracy, it still has to be determined in which input shape this weather data benefits the forecasting models the most. In the present paper, we investigate how transformations for weather data inputs, i. e., station-based and grid-based weather data, influence the accuracy of energy time series forecasts. The selected weather data transformations are based on statistical features, dimensionality reduction, clustering, autoencoders, and interpolation. We evaluate the performance of these weather data transformations when forecasting three energy time series: electrical demand, solar power, and wind power. Additionally, we compare the best-performing weather data transformations for station-based and grid-based weather data. We show that transforming station-based or grid-based weather data improves the forecast accuracy compared to using the raw weather data between 3.7 and 5.2%, depending on the target energy time series, where statistical and dimensionality reduction data transformations are among the best.

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Interpretable temporal-spatial graph attention network for multi-site PV power forecasting

Cited by 34 publications

References 23 publications

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

PV Power Forecasting in the Hexi Region of Gansu Province Based on AP Clustering and LSTNet

Using weather data in energy time series forecasting: the benefit of input data transformations

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