Space weather phenomena related to solar activity are usually considered a threat mainly at high geomagnetic latitudes, yet recent studies show that countries at lower latitudes are not immune to their effects. In this work, we analyse thirteen geomagnetic storms during Solar Cycle 24 (2010–2021) using a set of twelve heliospheric and geomagnetic parameters. We aim to reduce the dimensionality of this parameter space from \(\mathbb {R}^{12}\) to \(\mathbb {R}^{4}\) (or less) while preserving the essential physical information. To this end, we apply Principal Component Analysis (PCA) and show that the first 3–4 principal components explain at least \(80\%\) (typically 82– \(91\%\) ) of the total variance. The first component is consistently dominated by geomagnetic indices (Kp, Ec, Dst, ap, AE) and, in most storms, also by B, \(B_z\) and \(E_y\) , thus capturing the overall level of geomagnetic disturbance. The second and third components are mainly governed by solar wind properties, with robust pairings such as \((\textrm{SWs},\textrm{SWt})\) and \((B_z,E_y)\) , while \(B_y\) often forms a separate weakly coupled mode. We further use the leading components to fit simple regression models linking space-weather drivers to power-system loads and demonstrate that PCA can act as a diagnostic of unreliable interpolation in data with gaps. Our results indicate that PCA provides an efficient and physically interpretable reduction of heliogeomagnetic parameter space, facilitating the construction of statistical and machine-learning models for assessing and forecasting the impact of geomagnetic storms on technological infrastructure.
