Gulnaz Tolegenova

This study investigates self-supervised reconstruction of hidden values within an observed window for solar and wind energy time series, without forecasting beyond the window. The DualAxis architecture sequentially models temporal and cross-feature dependencies in 24 h windows, providing point reconstructions and a 96-dimensional representation. A chronological protocol separates training, validation, and retrospective test partitions without overlapping source rows and uses shared evaluation masks. DualAxis is compared with a Transformer, a gated recurrent unit (GRU) autoencoder, SAITS, and classical imputers. On the main test set with 30% random masking, the root mean squared error (RMSE) over ten signals, averaged over three training runs, is 0.0908 for DualAxis and 0.1031 for SAITS, whereas linear interpolation reaches 0.0774. Two independent datasets, rolling-origin validation, and structured missingness show that method rankings depend on evaluation conditions. A composite quality-control (QC) score combining reconstruction error and embedding outlierness changes substantially when the density component is removed or the model is retrained. Controlled corruptions do not confirm reliable automatic fault detection. Monte Carlo dropout uncertainty correlates moderately with error for both generation channels, but the intervals do not reach nominal coverage. The results delineate the capabilities and substantial limitations of multi-task non-forecasting analysis of renewable energy data.

Short-term forecasting of solar and wind power generation is critical for smart grid management but challenging due to non-stationarity and extreme generation events. This study addresses a multi-task learning problem: regression-based forecasting of power output and binary detection of extreme events defined by a quantile-based threshold (q = 0.90). A hybrid spatio-temporal model, DP-STH++, is proposed, implementing parallel causal fusion of LSTM, GRU, a causal Conv1D stack, and a lightweight causal transformer. The architecture employs regression and classification heads, while an uncertainty-weighted mechanism stabilizes multitask optimization in the regression tasks; extreme event detection performance is evaluated using AUC. Training and evaluation follow a leakage-safe protocol with chronological data processing, calendar feature integration, time-aware splitting, and training-only estimation of scaling parameters and extreme thresholds. Experimental results obtained with a one-hour forecasting horizon and a 24 h context window demonstrate that DP-STH++ achieves the best regression performance on the hold-out set (RMSE = 257.18, MAE = 174.86–287.90, MASE = 0.2438, R² = 0.9440) and the highest extreme event detection accuracy (AUC = 0.9896), ranking 1st among all compared architectures. In time-series cross-validation, the model retains the leading position with a mean MASE = 0.3883 and AUC = 0.9709. The advantages are particularly pronounced for wind power forecasting, where DP-STH++ simultaneously minimizes regression errors and maximizes AUC = 0.9880–0.9908.

Cities face a new challenge related to the growing number of their population. For the first time in history, most populations now live in a megacity. Based on the current situation, the city should implement innovative solutions and become the Smart City, supplying its citizens with systems that ensure security, health, resources, government, education, and other urban everyday systems. The purpose of this article is to investigate the Smart City model using multi-agent systems that provide city intelligence as the essential infrastructure for determining the model that can be reproduced and exported to create the Smart City. This article analyzes smart foreign cities using multi-agent systems, as well as the work of the multi-agents themselves.