Making the case for concentrated solar power
Dismissed by many in the solar industry as an overly-complex, outdated technology, concentrated solar power (CSP) is set for a comeback thanks to a scaled-down, modular approach.
New machine learning tech for short-term PV power generation forecasting
Researchers in China have applied a machine learning technology based on temporal convolutional networks in PV power forecasting for the first time. The new model reportedly outperforms similar models during all seasons.
Robotic measurement system assesses new materials for solar cells
Researchers in Japan have developed an automated system to perform photoabsorption and photoluminescence spectroscopy, optical microscopy, and white-light flash time-resolved microwave conductivity tests.
Weekend Read: Artificial opportunities
Artificial intelligence (AI) is hot right now and is finding central applications in homes and businesses as they move from simple grid connections to self-generation, energy storage, electric vehicle (EV) charging, and load-shifting revenue streams. With AI everywhere, what’s the difference between advanced control, via simple algorithms, and true intelligence?
AI tech for DC arc detection in PV systems
Scientists from China have developed a novel system that can detect DC arcs in PV installations through a back-propagation neural network. The novel technique reportedly ensures a detection time of less than 200 ms.
New model for day-ahead solar forecasting in areas with limited data
South Korean researchers have developed a long-term solar irradiance prediction method based on a reinforcement learning algorithm. They claim that the new model is able to forecast solar radiation for more than a year using just two weeks of solar radiation learning.
Computer vision for solar forecasts
A scientific review of solar forecasting with computer vision and deep-learning tech identifies areas for improvement and calls for more collaboration between project developers and grid operators.
Training deep machine learning to identify PV, solar thermal systems in aerial images
A Swedish research group has found that using deep machine learning to identify solar energy systems in aerial images may not be so accurate in non-densely populated countries such as Sweden. They have also found, however, that this technique may be trained via an iterative process and achieve satisfying results.
First AI-based control method for vanadium redox flow batteries
Australian researchers are proposing a novel, learning-based H∞ control method to enhance the performance of vanadium redox flow batteries in DC microgrids.
Novel AI-based tech to identify rooftop solar systems from aerial images
The model utilizes deep learning and image processing techniques and is said to offer “superior performance.” In the future, it might be able to differ between panels of PV and solar thermal systems.
