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Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison
The prediction of global solar radiation for the regions is of great importance in terms of
giving directions of solar energy conversion systems (design, modeling, and operation) …
giving directions of solar energy conversion systems (design, modeling, and operation) …
A current perspective on the accuracy of incoming solar energy forecasting
The state-of-the-art in the accuracy of solar resources forecasting is obtained by using
results reported in 1705 accuracy tests reported in several geographic regions (North …
results reported in 1705 accuracy tests reported in several geographic regions (North …
Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism
Photovoltaic power generation forecasting is an important topic in the field of sustainable
power system design, energy conversion management, and smart grid construction …
power system design, energy conversion management, and smart grid construction …
Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution …
Develo** an accurate and robust prediction of long-term average global solar irradiation
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
[HTML][HTML] Day-ahead solar irradiance forecasting for microgrids using a long short-term memory recurrent neural network: A deep learning approach
In microgrids, forecasting solar power output is crucial for optimizing operation and reducing
the impact of uncertainty. To forecast solar power output, it is essential to forecast solar …
the impact of uncertainty. To forecast solar power output, it is essential to forecast solar …
Convolutional graph autoencoder: A generative deep neural network for probabilistic spatio-temporal solar irradiance forecasting
Machine learning on graphs is an important and omnipresent task for a vast variety of
applications including anomaly detection and dynamic network analysis. In this paper, a …
applications including anomaly detection and dynamic network analysis. In this paper, a …
A novel data-driven approach for transient stability prediction of power systems considering the operational variability
Y Zhou, Q Guo, H Sun, Z Yu, J Wu, L Hao - International Journal of …, 2019 - Elsevier
Data driven methods are playing an increasingly important role in transient stability
assessment, primarily because of the availability of large annotated datasets. Nevertheless …
assessment, primarily because of the availability of large annotated datasets. Nevertheless …
RSAM: Robust self-attention based multi-horizon model for solar irradiance forecasting
With the widespread adoption of renewable energy sources in the smart grid era, there is an
utmost requirement to develop prediction models that can accurately forecast solar …
utmost requirement to develop prediction models that can accurately forecast solar …
Ultra-short-term multi-step probability interval prediction of photovoltaic power: A framework with time-series-segment feature analysis
L Zhang, Y He, H Wu, X Yang, M Ding - Solar Energy, 2023 - Elsevier
Power prediction can effectively mitigate the uncertainty in photovoltaic power generation,
enabling better operation and scheduling of power grids. Therefore, in this study, a multi …
enabling better operation and scheduling of power grids. Therefore, in this study, a multi …
Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries
The prediction of global solar radiation in a region is of great importance as it provides
investors and politicians with more detailed knowledge about the solar resource of that …
investors and politicians with more detailed knowledge about the solar resource of that …