Jorge Ballesteros

This work presents a robust preprocessing pipeline for photovoltaic (PV) time series forecasting aimed at improving the quality, consistency, and physical coherence of the input data used in predictive models. The proposed methodology integrates temporal lag correction, Fourier-based temporal enrichment, supervised and unsupervised outlier detection, and feature selection to adapt the preprocessing workflow to different operational conditions and data characteristics. The pipeline is validated using real-world data from two PV plants with different temporal resolutions and operating regimes. The results show that the proposed approach improves dataset coherence and strengthens the relationship between meteorological predictors and PV output, providing a reliable basis for subsequent forecasting tasks. In addition, an online forecasting validation over January 2025 shows that a Random Forest model using preprocessed inputs substantially reduces prediction errors compared with the same model using raw inputs, with MAE reductions of 54.2% for the Test Plant and 25.6% for the Production Plant, and corresponding RMSE reductions of 32.1% and 12.6%.

In this paper, we address the challenge of multivariate time-series forecasting through quantum-enhanced machine learning architectures. We propose adaptation strategies that extend variational quantum circuit models (VQC), traditionally constrained to univariate data, toward the multivariate setting, exploring both purely quantum and hybrid quantum-classical formulations. First, we extend and benchmark several VQC-based and hybrid models to systematically evaluate their ability to capture cross-variable dependencies. Building on these foundations, we introduce the iQTransformer, a novel quantum transformer architecture that integrates a quantum self-attention mechanism within the iTransformer framework, enabling a quantum-native representation of inter-variable relationships. Finally, we present a comprehensive empirical evaluation on both synthetic and real-world datasets, demonstrating that quantum-enhanced models can achieve competitive or superior forecasting accuracy with fewer trainable parameters and faster convergence than state-of-the-art classical and quantum baselines. These findings highlight the potential of quantum-enhanced architectures as efficient and scalable tools for advancing multivariate time-series forecasting.

Wind turbine power curve modeling is essential for wind power forecasting, turbine performance monitoring, and predictive maintenance. However, SCADA data often contain anomalies (e.g., curtailment, sensor faults), degrading the accuracy of power curve predictions. This paper presents a parameter-transfer learning strategy within a preprocessing and modeling framework that jointly optimizes anomaly detection (iForest, LOF, DBSCAN) and WTPC regressors (MLP, RF, GP) via a multi-metric objective adaptable to specific modeling requirements. In the source domain, hyperparameters are explored with randomized search, and in the target domain, transferred settings are refined with Bayesian optimization. Applied to real SCADA from different locations and turbine models, the approach achieves a 90% reduction in optimization iterations and consistently improves target domain performance according to the objective, with no observed loss when comparable source and target turbines differ in site or rated power. Gains are larger for more similar source–target pairs. Overall, the approach yields a practical model-agnostic pipeline that accelerates preprocessing and modeling while preserving or improving fit, particularly for newly installed turbines with limited data.