A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
A review on artificial intelligence applications for grid-connected solar photovoltaic systems
The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV)
systems, due to the increasing computational power, tools and data generation. The …
systems, due to the increasing computational power, tools and data generation. The …
Day-ahead photovoltaic forecasting: A comparison of the most effective techniques
We compare the 24-hour ahead forecasting performance of two methods commonly used for
the prediction of the power output of photovoltaic systems. Both methods are based on …
the prediction of the power output of photovoltaic systems. Both methods are based on …
Ensemble approach of optimized artificial neural networks for solar photovoltaic power prediction
The use of data-driven ensemble approaches for the prediction of the solar Photovoltaic
(PV) power production is promising due to their capability of handling the intermittent nature …
(PV) power production is promising due to their capability of handling the intermittent nature …
Hourly forecasting of the photovoltaic electricity at any latitude using a network of artificial neural networks
Nowadays, special attention is paid to the importance of using photovoltaic (PV) systems to
tackle the problem of climate change and the energy crisis. Artificial intelligence is currently …
tackle the problem of climate change and the energy crisis. Artificial intelligence is currently …
Extreme learning machines for solar photovoltaic power predictions
The unpredictability of intermittent renewable energy (RE) sources (solar and wind)
constitutes reliability challenges for utilities whose goal is to match electricity supply to …
constitutes reliability challenges for utilities whose goal is to match electricity supply to …
[HTML][HTML] Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting
Accurate forecasting for photovoltaic power generation is one of the key enablers for the
integration of solar photovoltaic systems into power grids. Existing deep-learning-based …
integration of solar photovoltaic systems into power grids. Existing deep-learning-based …
A recursive ensemble model for forecasting the power output of photovoltaic systems
L Liu, M Zhan, Y Bai - Solar Energy, 2019 - Elsevier
Solar power provides a clean and renewable energy source. However, unlike many
conventional sources, Photovoltaic (PV) power generation is of high volatility and …
conventional sources, Photovoltaic (PV) power generation is of high volatility and …
A review on machine learning applications for solar plants
E Engel, N Engel - Sensors, 2022 - mdpi.com
A solar plant system has complex nonlinear dynamics with uncertainties due to variations in
system parameters and insolation. Thereby, it is difficult to approximate these complex …
system parameters and insolation. Thereby, it is difficult to approximate these complex …
Ensemble machine learning for predicting the power output from different solar photovoltaic systems
In this paper, ensemble-based machine learning models with gradient boosting machine
and random forest are proposed for predicting the power production from six different solar …
and random forest are proposed for predicting the power production from six different solar …