A Long Short-Term Memory-Based Solar Irradiance Prediction Scheme Using Meteorological Data

Golam, Mohtasin; Akter, Rubina; Lee, Jae-Min; Kim, Dong-Seong

doi:10.1109/lgrs.2021.3107139

Cited by 25 publications

(7 citation statements)

References 11 publications

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“…The effectiveness of a solar prediction model is assessed using commonly used error metrics (like R2 score, MAE, MSE, and RMSE) and the results are depicted in Table 1 . The previous research work [ 15 ] reported that the RMSE, MSE, and MAE are 0.057, 0.0033, and 0.0413, respectively, while other work [ 4 ] reported that the RMSE and R2 values are 3.17 and 0.93, respectively. Table 1 illustrates the considerable enhancements in error metrics (RMSE, MSE, MAE, and R 2 ) of the proposed work compared to the aforementioned reported work.…”

Section: Resultsmentioning

confidence: 99%

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Solar forecasting for a PV-battery powered DC system

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

“…The MPPT [ [13] , [14] , [15] ] is a significant factor in enhancing the effectiveness of the PV system, and PV power management systems rely heavily on accurate predictions of solar irradiation. To maximize the output power from PV cells, accurately tracking the system's maximum power point (MPP) is essential.…”

Section: Introductionmentioning

confidence: 99%

Solar forecasting for a PV-battery powered DC system

et al. 2023

Heliyon

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“…3. The prediction of PV generation and load demand is implemented with LSTM (Long Short-Term Memory) neural network and outputs the prediction results per minute [7]. Then, the prediction of PV generation and load demand would be given for calculation of residual energy in (2).…”

Section: Management Strategymentioning

confidence: 99%

Enhancement of System Resilience for Islanded Microgrid with Clean Energy

Chen,

Zhu,

Chen

et al. 2024

E3S Web Conf.

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Enhancement of system resilience of microgrid is important when the system is operated in the islanded mode. The commonly used clean energy in microgrid is the intermittent renewable energy which would deteriorate the system resilience of microgrid. To solve this problem, the residual energy pre-served in the energy storage system is introduced in this paper to extend the time duration of fundamental load demand when the power outage is present. The experiment is implemented in the demo system in the campus of National Central University to verify the performance of proposed management strategy.

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“…In [119], the clearness-index has been proposed, which can improve the accuracy of the forecasting on cloudy conditions. Additionally, more details and methods about the prediction of the solar irradiance can be found in [120][121][122][123][124][125].…”

Section: Solar Irradiance Predictionmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

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A Long Short-Term Memory-Based Solar Irradiance Prediction Scheme Using Meteorological Data

Cited by 25 publications

References 11 publications

Solar forecasting for a PV-battery powered DC system

Solar forecasting for a PV-battery powered DC system

Enhancement of System Resilience for Islanded Microgrid with Clean Energy

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

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