Machine Learning Enhances Solar Power Forecast Accuracy

by Simon Mansfield

Sydney, Australia (SPX) Feb 18, 2025

As solar power becomes a more significant component of the global energy grid, improving the accuracy of photovoltaic (PV) generation forecasts is crucial for balancing supply and demand. A recent study published in Advances in Atmospheric Sciences examines how machine learning and statistical techniques can enhance these predictions by refining errors in weather models.

Since PV forecasting depends heavily on weather predictions, inaccuracies in meteorological models can impact power output estimates. Researchers from the Institute of Statistics at the Karlsruhe Institute of Technology investigated ways to improve forecast precision through post-processing techniques. Their study evaluated three methods: adjusting weather forecasts before inputting them into PV models, refining solar power predictions after processing, and leveraging machine learning to predict solar power directly from weather data.

“Weather forecasts aren’t perfect, and those errors get carried into solar power predictions,” explained Nina Horat, lead author of the study. “By tweaking the forecasts at different stages, we can significantly improve how well we predict solar energy production.”

The study found that applying post-processing techniques to power predictions, rather than weather forecasts, yielded the most significant improvements. While machine learning models generally outperformed conventional statistical methods, their advantage was marginal in this case, likely due to the constraints of the available input data. Researchers also highlighted the importance of including time-of-day information in models to enhance forecast accuracy.

“One of our biggest takeaways was just how important the time of day is,” said Sebastian Lerch, corresponding author of the study. “We saw major improvements when we trained separate models for each hour of the day or fed time directly into the algorithms.”

A particularly promising approach involves bypassing traditional PV models altogether by using machine learning algorithms to predict solar power directly from weather data. This technique eliminates the need for detailed knowledge of a solar plant’s configuration, relying instead on historical weather and performance data for training.

The findings pave the way for further advancements in machine learning-based forecasting, including the integration of additional weather variables and the application of these methods across multiple solar installations. As renewable energy adoption accelerates, improving solar power forecasting will be key to maintaining grid stability and efficiency.

Research Report:Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning

Related Links

Institute of Atmosphere at CAS

All About Solar Energy at SolarDaily.com

The next-generation solar cell is fully recyclable

by Robert Schreiber

Berlin, Germany (SPX) Feb 18, 2025

Researchers at Linkoping University have developed a groundbreaking method for recycling all components of a perovskite solar cell without the use of hazardous solvents. The process ensures that recycled solar cells maintain the same efficiency as newly manufactured ones, marking a significant step toward sustainable solar technology. The primary solvent used in this method is water, offering an environmentally friendly alternative to conventional recycling processes.

With the anticipated surge in electricity demand due to the expansion of artificial intelligence and the electrification of transportation, sustainable energy sources must advance to prevent further environmental impact. Solar power has long been considered a viable renewable energy source, with silicon-based panels dominating the market for over three decades. However, as first-generation silicon panels reach the end of their lifespan, waste management poses a major challenge.

“There is currently no effective technology to handle the waste from silicon solar panels. As a result, outdated panels are being discarded in landfills, leading to vast amounts of electronic waste,” explained Xun Xiao, postdoctoral researcher at Linkoping University’s Department of Physics, Chemistry, and Biology (IFM).

Feng Gao, a professor of optoelectronics at the same department, emphasized the importance of considering recyclability in emerging solar technologies: “If we don’t have a recycling solution in place, perhaps we shouldn’t introduce new solar cell technologies to the market.”

Perovskite solar cells are among the most promising alternatives for next-generation solar technology. These cells are lightweight, flexible, and transparent, making them suitable for various surfaces, including windows. Additionally, they achieve energy conversion efficiencies of up to 25 percent, rivaling silicon-based solar cells.

“Many companies are eager to commercialize perovskite solar cells, but we must ensure that they do not contribute to landfill waste. Our project introduces a method where all components of perovskite solar cells can be reused without sacrificing performance,” said Niansheng Xu, postdoctoral researcher at Linkoping University.

Although perovskite solar cells have a shorter lifespan than their silicon counterparts, it is crucial to develop an efficient and environmentally friendly recycling process. Additionally, these cells contain a small amount of lead, essential for high efficiency but requiring proper handling to prevent environmental contamination. In many parts of the world, manufacturers are legally obligated to recycle end-of-life solar cells sustainably.

Existing recycling methods for perovskite solar cells often rely on dimethylformamide, a toxic and potentially carcinogenic solvent commonly found in paint removers. The Linkoping researchers have devised an innovative approach that replaces this hazardous chemical with water, significantly reducing environmental risks. This method enables the recovery of high-quality perovskite materials from the water-based solution.

“We can recover every component-the glass covers, electrodes, perovskite layers, and charge transport layers,” Xiao added.

The next phase of research will focus on scaling up this process for industrial applications. In the long term, scientists believe that perovskite solar cells will become a key component of the global energy transition, particularly as supporting infrastructure and supply chains evolve.

Research Report:Aqueous based recycling of perovskite photovoltaics

Related Links

Linkoping University

All About Solar Energy at SolarDaily.com

Scientists Probe Declining Earbud Battery Longevity

by Clarence Oxford

Los Angeles CA (SPX) Feb 05, 2025

Have you ever noticed how electronic devices, including wireless earbuds, seem to lose battery capacity faster the longer you use them? An international research team from The University of Texas at Austin set out to examine this familiar issue, known as battery degradation, by focusing on the earbuds that many people rely on daily. Through a series of x-ray, infrared, and other imaging approaches, the researchers investigated the hidden complexities behind these tiny devices and revealed why their battery life declines over time.

“This started with my personal headphones; I only wear the right one, and I found that after two years, the left earbud had a much longer battery life,” said Yijin Liu, an associate professor in the Cockrell School of Engineering’s Walker Department of Mechanical Engineering, who led the new research published in Advanced Materials. “So, we decided to look into it and see what we could find.”

Their analysis showed that crucial earbud features – like the Bluetooth antenna, microphones, and circuits – compete with the battery in a very confined space, producing a microenvironment that is less than ideal. This situation results in a temperature gradient that damages the battery over time, with different sections of the cell experiencing variable temperatures.

Real-world factors also complicate matters. Frequent changes in climate, shifts in air quality, and a host of other environmental variables challenge the battery’s resilience. While cells are generally designed to endure harsh conditions, constant fluctuations can take their toll.

These discoveries highlight the importance of considering how batteries interact with devices such as phones, laptops, and even electric vehicles. Packaging solutions, strategic design decisions, and adaptations for user habits may all play a role in extending battery performance.

“Using devices differently changes how the battery behaves and performs,” said Guannan Qian, the first author of this paper and a postdoctoral researcher in Liu’s lab. “They could be exposed to different temperatures; one person has different charging habits than another; and every electric vehicle owner has their own driving style. This all matters.”

In conducting this study, Liu and his team worked closely with UT’s Fire Research Group, led by mechanical engineer Ofodike Ezekoye. They paired infrared imaging methods with their in-house x-ray technology at UT Austin and Sigray Inc. To expand their scope, they then teamed up with some of the world’s most advanced x-ray facilities.

Their collaborators included researchers from SLAC National Accelerator Laboratory’s Stanford Synchrotron Radiation Lightsource, Brookhaven National Laboratory’s National Synchrotron Light Source II, Argonne National Laboratory’s Advanced Photon Source, and the European Synchrotron Radiation Facility (ESRF) in France. These partnerships allowed them to observe battery behavior under more authentic operating conditions.

“Most of the time, in the lab, we’re looking at either pristine and stable conditions or extremes,” said Xiaojing Huang, a physicist at Brookhaven National Laboratory. “As we discover and develop new types of batteries, we must understand the differences between lab conditions and the unpredictability of the real world and react accordingly. X-ray imaging can offer valuable insights for this.”

Looking ahead, Liu says his team will continue analyzing battery performance in the settings people experience every day. They plan to expand their approach to larger batteries, such as those in smartphones, laptops, and electric vehicles, to learn more about their degradation patterns.

Research Report:In-device Battery Failure Analysis

Related Links

University of Texas at Austin

Powering The World in the 21st Century at Energy-Daily.com