Solar radiation forecasting based on convolutional neural network and ensemble learning
Nowadays, we are moving forward to more sustainable energy production systems based
on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In …
on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In …
Analysis and impact evaluation of missing data imputation in day-ahead PV generation forecasting
T Kim, W Ko, J Kim - Applied Sciences, 2019 - mdpi.com
Over the past decade, PV power plants have increasingly contributed to power generation.
However, PV power generation widely varies due to environmental factors; thus, the …
However, PV power generation widely varies due to environmental factors; thus, the …
Missing value imputation for short to mid-term horizontal solar irradiance data
H Demirhan, Z Renwick - Applied Energy, 2018 - Elsevier
Improving the accuracy of solar irradiance forecasting has become crucial since the use of
solar energy power has become more accessible due to increased efficiency and decreased …
solar energy power has become more accessible due to increased efficiency and decreased …
Short-term forecasting of global solar irradiance in tropical environments with incomplete data
Electricity access is a common issue around the world. Many countries that face this problem
are located in the tropical region and solar energy might be an alternative to mitigate this …
are located in the tropical region and solar energy might be an alternative to mitigate this …
[HTML][HTML] Speckle noise reduction for structural vibration measurement with laser Doppler vibrometer on moving platform
Speckle noise is a major problem for structural vibration measurements with Laser Doppler
vibrometer on moving platform (LDVom) due to its highly random, frequent, and broadband …
vibrometer on moving platform (LDVom) due to its highly random, frequent, and broadband …
BERT (Bidirectional Encoder Representations from Transformers) for missing data imputation in solar irradiance time series
The availability of solar irradiance time series without missing data is an ideal scenario for
researchers in the field. However, it is not achievable for a variety of reasons, such as …
researchers in the field. However, it is not achievable for a variety of reasons, such as …
A case study in the tropical region to evaluate univariate imputation methods for solar irradiance data with different weather types
NB Mohamad, AC Lai, BH Lim - Sustainable Energy Technologies and …, 2022 - Elsevier
A complete dataset of ground-measured Global Horizontal Solar Irradiance (GHI) is vital for
the design and performance assessment of a photovoltaic (PV) system. Hence, imputing the …
the design and performance assessment of a photovoltaic (PV) system. Hence, imputing the …
Short-term nonlinear autoregressive photovoltaic power forecasting using statistical learning approaches and in-situ observations
Due to the low total cost of production, Photovoltaic energy constitutes an important part of
the renewable energy installed in the world. However, photovoltaic energy is volatile in …
the renewable energy installed in the world. However, photovoltaic energy is volatile in …
Missing-data tolerant hybrid learning method for solar power forecasting
Solar power forecasting is a key task in modern power grid operation, which can be
achieved by machine learning-based methods. Due to multiple practical issues, the data …
achieved by machine learning-based methods. Due to multiple practical issues, the data …
A novel hybrid transformer-based framework for solar irradiance forecasting under incomplete data scenarios
Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy.
However, in real-world scenarios, complex irradiance patterns and prevalent incomplete …
However, in real-world scenarios, complex irradiance patterns and prevalent incomplete …