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For example, forecasting models can be used to assess the impact of changes in solar irradiance or weather patterns on microgrid operations or to identify opportunities for demand-side management . Moreover, to effectively implement solar power generation forecasting models in microgrid operations, several guidelines can be followed:
Solar is a significant renewable energy source. Solar energy can provide for the world''s energy needs while minimizing global warming from traditional sources. Forecasting …
This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power …
In the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players in the global effort to curtail greenhouse gas …
The solar power generation (renewable energy) is the cleanest form of energy generation method and the solar power plant has a very long life and also is maintenance-free, but due to the high ...
PV solar power generation has intrinsic characteristics related to the climatic variables that cause intermittence during the generation process, promoting instabilities and …
This research tackles this issue by deploying machine learning models, specifically recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU), to …
[1.] Sandeep k., Viay K. Garg., "A Hybrid Model of Solar Wind Power Generation System," International Journal of Advanced Research in Electrical Electronics and Instrumentation …
Large solar farms in the Sahara Desert could redistribute solar power generation potential locally as well as globally through disturbance of large-scale atmospheric …
PV solar power generation has intrinsic characteristics related to the climatic variables that cause intermittence during the generation process, promoting instabilities and insecurity in the ...
Solar energy is an environmentally friendly and renewable energy source. In recent years, solar power plants have a large share in electricity production around the world. …
This study aims to present deep learning algorithms for electrical demand prediction and solar PV power generation forecasting. Therefore, we proposed a novel multi-objective hybrid model named FFNN …
Solar power forecasting is very usefull in smooth operation and control of solar power plant. Generation of energy by a solar panel or cell depends upon the doping level and design of …
To bridge this research gap, there are a number of different forecasting models that can be used to predict solar power generation. Two of the most popular models are LGBM and KNN. LGBM is a machine learning algorithm that has been shown to be effective for a variety of forecasting tasks.
A comparative study of machine learning algorithms was proposed for solar power generation forecasting, which compared multiple machines learning algorithms, including LGBM, KNN, artificial neural networks (ANN) and support vector regression (SVR). The analysis involved evaluating the accuracy, precision, and reliability of these algorithms.
Solar energy is a widely accessible, clean, and sustainable energy source. Solar power harvesting in order to generate electricity on smart grids is essential in light of the …
Owing to the persisting hype in pushing toward global carbon neutrality, the study scope of atmospheric science is rapidly expanding. Among numerous trending topics, energy …
This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of forecasting …
Considering the characteristics of wind speed, module temperature, ambient and solar radiation, Akhter et al. 13 constructed an RNN-LSTM model to predict PV power …
Table 8. Comparison with the literature on PV power generation forecasting. that the proposed hybrid model is better than those in the literature with minimum error and highest regression. 4. Conclusion This study aims to present deep learning algorithms for electrical demand prediction and solar PV power generation forecasting.
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