The study, published in the journal Results in Engineering, was conducted by Madiyar Nurgaliyev, Batyrbek Zholamanov, and colleagues at Al-Farabi Kazakh National University in Almaty, with collaborators including Saad Mekhilef. The team built a complete Internet of Things measurement system on the roof of the university’s Physics and Engineering Faculty, combining a mini spectrometer, a fisheye sky camera, a pyranometer, temperature and humidity sensors, and a current sensor, all orchestrated by a Raspberry Pi 4 microcomputer that streamed data to a cloud server. Every minute throughout October 2025, the rig recorded not only how much sunlight was falling, but what colors that sunlight was made of.

That spectral detail matters more than most people realize. Photovoltaic cells do not respond equally to all wavelengths of light; silicon crystals convert blue photons and red photons with different efficiencies, and the spectral composition of sunlight shifts as clouds, aerosols, and water vapor scatter and absorb radiation unevenly across the color range. Conventional forecasting methods treat solar irradiance as a single integrated value, which means they implicitly assume the sun’s spectrum stays constant. It does not. When clouds roll in, the blue end of the spectrum can be suppressed while the red end holds relatively steady, and a panel’s output changes in ways that a simple brightness number cannot capture.

The researchers’ central insight was to split the forecasting problem into two stages. First, a deep neural network predicts how the solar spectrum itself will evolve over the coming minutes, wavelength by wavelength. Then, a second regression model translates those predicted spectra into the electrical current a solar array will generate. This two-stage design elegantly separates two sources of nonlinearity that are usually tangled together: the weather-driven changes in incoming light, and the panel’s own internal physics. The team’s hypothesis is that a solar cell’s quantum efficiency, its wavelength-by-wavelength conversion efficiency, depends only on the material it is made of, not on the season, the cloud cover, or the time of day. If that holds, then once you can forecast the spectrum, the panel’s response follows from physics rather than from a black box trained on one specific installation.

To test whether spectral information genuinely helps, the team ran a systematic horse race. Four forecasting scenarios were compared on identical footing: one using only solar irradiance and statistical cloud metrics, one adding the measured spectrum to irradiance, one combining irradiance with sky images processed by convolutional neural networks, and the proposed two-stage spectral approach. Each scenario was evaluated across six forecast horizons, from one minute out to sixty minutes, using the same metrics: the coefficient of determination R-squared, mean absolute error, mean squared error, and root mean squared error. The architectures included bidirectional LSTMs, gated recurrent units, temporal convolutional networks, and hybrid combinations, all tuned with Bayesian optimization through the Optuna library.

The results deliver a clear verdict for the ultra-short term. The proposed spectral approach, implemented with a multilayer perceptron, achieved the best current forecasting quality of all tested configurations for horizons up to roughly fifteen minutes. At the one-minute mark, it reached an R-squared of 0.9706, climbing from 0.9316 for the best irradiance-only model, and it maintained a decisive lead through five, ten, and fifteen minutes. Five-fold cross-validation confirmed the numbers, with R-squared of 0.9683 plus or minus 0.0043 at one minute and tight confidence intervals throughout. Ten independent training runs with different random seeds showed the model is robust, not a lucky artifact of one initialization.

But the picture changes as the horizon stretches out. By thirty to sixty minutes, the spectral advantage fades, and a model that forecasts from sky images, using a gated recurrent unit fed with features extracted by a ResNet-50 classifier, takes the crown, reaching an R-squared of 0.6636 at sixty minutes. The reason is physical: within fifteen minutes, the atmosphere has enough inertia that the spectrum’s recent history is a reliable guide, but beyond half an hour, cloud masses advected by wind can completely transform the radiation regime, and the spectral time series alone simply cannot see that coming. Sky images, which capture the visual structure of approaching clouds, retain predictive power longer. Notably, the researchers also found that simply bolting measured spectral features onto a direct forecasting model does not guarantee improvement, which underscores that how spectral information is used matters as much as whether it is used at all.

The team also mapped where in the spectrum the magic happens. A permutation-based sensitivity analysis revealed that the model leans most heavily on shorter wavelengths between roughly 350 and 425 nanometers, with additional peaks of importance around 470 to 530, 620 to 680, and 810 to 830 nanometers. This makes physical sense, since short-wavelength blue light is the most sensitive to atmospheric scattering, and therefore carries the richest information about changing sky conditions. The researchers also quantified how forecast quality decays with horizon, fitting the R-squared decline with an exponential function and the error growth with a quadratic one, both with fitting quality above 0.995. Error analysis across operating regimes showed that transient, fast-changing conditions are the hardest to predict, while cloudy conditions are easiest at short horizons and high-radiation conditions win out at longer ones.

The practical implications are significant for microgrid management and concentrated photovoltaic systems, where the spectral composition of direct sunlight plays an outsized role. Ultra-short-term forecasts in the one-to-fifteen-minute range are exactly the timescale on which grid operators must balance generation and demand in real time, and a forecast that separates weather effects from panel physics could transfer more readily between installations than models trained end-to-end on a single plant’s history. The approach could also benefit high-concentrator photovoltaic systems, whose multilayer cells are especially sensitive to spectral shifts.

The researchers are candid about limitations: the dataset covers a single month at a single location, so seasonal variability and geographic generalization remain untested, and sensor noise from the spectrometer, camera, and current sensor introduces uncertainty that partially mingles with model error. Future work will extend the dataset to year-round observations and additional sites, and will explore integrating spectral data with sky imagery and meteorological variables. Still, the core result stands: for the critical first fifteen minutes, the color of sunlight is a better predictor of solar power than its brightness alone, and teaching a neural network to see that color ahead of time is a genuinely new tool for keeping solar-heavy grids stable.

Subject of Research: Ultra-short-term photovoltaic power forecasting using solar spectral irradiance and deep learning

Article Title: New PV output power forecasting approach using solar spectrum and deep learning for ultra-short time horizon

Article References: Nurgaliyev, M., Zholamanov, B., Mohammed, K. K., Mekhilef, S., Sybanbayeva, D., Saymbetov, A., Kuttybay, N., Dosymbetova, G., Almen, D., Valiyev, B., Kaniyev, Y., Kapparova, A., Koshkarbay, N., Bolatbek, A., & Orynbassar, S. (2026). New PV output power forecasting approach using solar spectrum and deep learning for ultra-short time horizon. Results in Engineering, 32, Article 113574. https://doi.org/10.1016/j.rineng.2026.113574

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113574

Keywords: photovoltaics, solar spectrum, deep learning, ultra-short-term forecasting, clear sky index, sky images, multilayer perceptron, GRU, temporal convolutional network, quantum efficiency, microgrid management, IoT sensors