The increasing integration of photovoltaic systems into modern power grids requires forecasting models that not only provide accurate predictions but also reliable uncertainty quantification under evolving operating conditions. In this paper, we propose an Out-of-distribution-aware time series conformal prediction framework with adaptive retraining, designed to address key limitations of standard conformal prediction methods in temporally dependent and dynamically changing environments. The framework is built upon the Ensemble batch prediction intervals method, which enables distribution-free uncertainty quantification without relying on a fixed calibration set, making it particularly suitable for time series applications. To ensure robustness to distribution shifts, a conformal out-of-distribution detection module is incorporated, where out-of-distribution detection is formulated as a hypothesis testing problem and enhanced through calibration-conditional p-values obtained via the Simes correction, providing conservative false-positive control intended to limit unnecessary model retraining. The proposed framework demonstrates superior performance compared to state-of-the-art approaches in uncertainty quantification, while conformal out-of-distribution detection reduces false positives and the adaptive retraining mechanism ensures effective adaptation to evolving data distributions in real-world scenarios.

This paper discusses the process of training a neural network intended for predicting power generation in a solar power plant based on meteorological data and the historical data of power measurement readings. Initially, the neural network was trained on a wide array of different solar and weather quantities as inputs (26 in total), but since the prediction errors of this model were considerably large, the model was retrained, but on a much smaller subset of critical input variables, which resulted in an increase of accuracy where MAE dropped significantly.

In this paper, we firstly investigate the relationships between weather conditions and solar plant power output during winter and near-winter months (March and October) and afterwards, we create multiple neural network models for predicting the solar power output based on weather parameters which are prominent during such seasons. The motivation for this work comes from the fact that successful prosumer management necessitates accurate predictions of solar outputs, especially during winter due to effects of snow soiling of the PV cells and lower air temperature on the output. Moreover, we imply that a neural network model trained on data collected for a particular season will be more accurate than a model trained on data collected all year-round, due to significant differences in data distribution between different year seasons. To confirm this implication, we create several groups of such models and compare their prediction accuracy to a more complex model from our previous work which was trained on an all-year round dataset. The results show that the mean-absolute error (MAE) of the most accurate winter-specialized model is 76.85 kW, which is around 5% lower than the MAE of the full dataset model (80.08 kW). This accuracy improvement sets path for further research in creating segmented neural network models for predicting power output under different weather conditions.

This study investigates Multivariate Adaptive Regression Splines (MARS) for predicting hourly photovoltaic power output, comparing performance against Long Short-Term Memory (LSTM) networks. Using three years of data from a 156 kW installation in southeastern Serbia, MARS achieved superior accuracy across mean absolute error, root mean square error, and coefficient of determination. LSTM excelled during stable clear-sky conditions with smooth temporal patterns, while MARS handled rapid weather transitions better through piecewise linear basis functions. MARS automatically identified predictive value in snow depth, precipitable water, and relative humidity— variables excluded from LSTM due to weak linear correlation. These findings demonstrate MARS as an effective, interpretable alternative for solar forecasting involving threshold effects and conditional dependencies.

This paper presents phase angle control and RPM measurement of a universal electric motor using a CAN interface. The system consists of two devices based on a microcontroller ATmega328P that are connected through a CAN bus for sending and receiving data for phase angle regulation and speed measurement. The system is implemented and checked in the laboratory using an oscilloscope and logic analyzer. Also, the obtained results and signal diagrams are presented as well.