Ozdes, CelikEdiger, Volkan S.Eroglu, Deniz2025-11-152025-11-1520252662-9992https://doi.org/10.1057/s41599-025-04904-xhttps://hdl.handle.net/20.500.12469/7578Impacts from natural disasters, government decisions and public's reactions can significantly alter societal daily routines. These effects resonate in systems where individual contributions, such as energy consumption, serve as indirect indicators of societal welfare and living standards. Preparedness for unforeseen events is crucial to enhancing societal well-being. Thus, analysing historical data for unexpected critical transitions and forecasting future occurrences is paramount. Recurrence properties of gross monthly electricity consumption in the United States of America and Turkey are examined, revealing coinciding critical periods with extreme regimes identified by a determinism time series. An ensemble of neural network proxies is then employed to forecast critical periods within a limited time frame, enabling the anticipation of similar occurrences. Validation of this approach demonstrates high predictive performance when measured quantities adequately reflect underlying system dynamics. Predictions based on electricity consumption data suggest potential systemic and socioeconomic crises for both nations within one year, with probabilities, 85% for the US and 32% for Turkey.eninfo:eu-repo/semantics/openAccessArticle10.1057/s41599-025-04904-x2-s2.0-105018524510