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This paper presents insight into how rural young adults in the rural areas of Oost-Groningen, The Netherlands, and Südharz, Germany, deal with their rural identity with regard to different future horizons. This paper applies future horizons instead of intended future plans to emphasize the open and uncertain nature of young adults' aspired future. Based on 15 biographical interviews and a survey to geographically contextualize these interviews, the results show how rural young adults can have parallel future horizons to maintain several options open in which they all aim to preserve a rural identity. This paper illustrates how rural young adults compromise a rural identity with aspirations elsewhere in a staying or rural horizon elsewhere. In addition, the results show how they deal with rural identities by further internalizing and externalizing their rural identities with variations of embracing aspects of a rural identity with regard to different future horizons. The paper concludes that young adults can be considered, however also out of uncertainty about the future, as active participants of their future who apply parallel and temporal horizons and still preserve a rural identity. • Future horizons capture the open nature of young adults' futures. • Young adults apply parallel and temporal horizons to preserve a rural identity. • Rural young adults refocus their rural identity to align it with parallel aspirations. • Regional contexts matter in how rural identities are changed for future horizons. • Young adults can be considered as active participants in shaping their futures. [ABSTRACT FROM AUTHOR]

Integrating enormous quantities of wind energy into the electrical system requires precise planning and forecasting. This paper presents a novel framework for wind power forecasting, which establishes a new standard for accuracy and reliability. It uses advanced deep neural networks and a stacking ensemble mechanism to make probabilistic forecasts. These forecasts address wind speed volatility and variability, which continue to be key issues in producing consistently accurate wind power forecasts despite advances in deep learning. Base learners generate forecasts that include lower and upper bounds, as well as median prediction intervals. The Huber regressor, also known as the Meta-learner, combines projections from many models to reduce susceptibility to extreme values. Expanding window cross-validation is used in performance evaluation, with a focus on Mean Absolute Error, Mean Squared Error (MSE), Symmetric Mean Absolute Percentage Error, Prediction Interval Coverage Probability, and Average Interval Width for one, two, and three step ahead predictions. The model's stability is measured by the standard deviation of MSE throughout each validation window. Additionally, the Diebold–Mariano test is used to validate the Meta-learner's predictive accuracy in contrast to other advanced models and the seasonal naïve benchmark. This study found that the meta model outperformed all other models in eight of the nine forecasts tested, indicating its effectiveness in medium-range forecasting. In Germany, it exceeded Temporal Fusion Transformer by two and three steps ahead, with 22% and 34% improvements, respectively. Indeed, this study not only provides a reliable prediction tool, but also lays foundations for efficient and effective energy management and policy planning in the renewable energy industry. • Deep neural network architectures are applied to probabilistic wind power forecasting. • Transformer and artificial neural network approaches combined with conformal prediction intervals is proposed. • A window cross-validation scheme on wind power forecasting is evaluated. • Wind power data from Germany, the UK, and the Netherlands are used for forecasting purposes. • SHAP and LIME analyses perform the explanatory analysis of the variables in the meta-learner's predictions. [ABSTRACT FROM AUTHOR]