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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]

• Soil reflectance composites were generated with Sentinel-2 L2A data. • A multi-channel CNN learning framework for soil organic carbon prediction is proposed. • A dual input deep learning architecture improves the predictions. • The framework produced results that can be interpreted. The Copernicus Sentinel-2 multispectral imagery data may be aggregated to extract large-scale, bare soil, reflectance composites, which enable soil mapping applications. In this paper, this approach was tested in the German federal state of Bavaria, to provide estimations for soil organic carbon (SOC). Different temporal ranges were considered for the generation of the composites, including multi-annual and seasonal ranges. A novel multi-channel convolutional neural network (CNN) is proposed. By leveraging the advantages of deep learning techniques, it utilizes complementary information from different spectral pre-treatment techniques. The SOC predictions indicated little dissimilarity amongst the different composites, with the best performance attained for the six-year composite containing only spring months (RMSE = 12.03 g C · kg−1, R2 = 0.64, RPIQ = 0.89). It has been demonstrated that these outcomes outperform other well-known machine learning techniques. An ablation analysis was accordingly performed to evaluate the interplay of the CNN's different components to disentangle the advantages of each aspect of the proposed framework. Finally, a DUal inPut deep LearnIng architecture, named DUPLICITE, is proposed, which concatenates deep spectral features derived from the CNN mentioned earlier, as well as topographical and environmental covariates through an artificial neural network (ANN) to exploit their complementarity. The proposed approach was demonstrated to provide an improvement in the overall prediction performance (RMSE = 11.60 gC · kg−1, R2 = 0.67, RPIQ = 0.92). [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is regarded worldwide as one of the most important technologies of the future. In the international context, Germany is not considered a pioneer in the field of AI, and the implementation of AI technologies in German companies is also rather slow. Since the German economy is mainly driven by SMEs, the implementation of AI in SMEs is a key success factor of the national economy. It is known from general studies on digitalization that German SMEs are rather skeptical about new technologies. This paper discusses the implementation prospects of AI in German SMEs based on an empirical survey conducted in 2020 among 283 companies. It shows that the SMEs surveyed tend to use more traditional technologies such as rule-based systems and see the lack of employee qualifications in particular as an implementation hurdle. [ABSTRACT FROM AUTHOR]

This paper deals with possibilities of targeting energy efficiency potentials in German companies by delivering information and support within a cooperative management system “Learning Energy Efficiency Networks” (LEEN). Information deficits are pointed out as a relevant barrier to implementing energy efficiency measures in literature that can be aimed at by energy programmes such as audits. Our concept combines aspects of cooperation, shared experiences and moderated learning processes. The programme sought to go beyond the sole provision of consultation, by qualifying companies to decide and act on implementing energy efficiency measures by delivering the individual practical know-how they need. After having analysed evaluation data of several hundred companies, our results show that the participants were mainly motivated by the need for practical knowledge and specific information. The networks could well satisfy this need. The benefit for the participants in such delivered information was also reflected in the decreasing of the barrier of imperfect information and the programme enabled companies to make informed decisions on energy efficiency measures. But the gain of the programme was not solely restricted on an increase in knowledge: The majority of participants reported implementation of suggested measures, which would not have been implemented without the programme. Hence, LEEN can be indicated as a policy instrument enabling informed decisions on efficiency measures and supporting their implementation. Possibly tailoring the programme to different target groups and aiming at dismantling other barriers directly in addition to tackling information deficits could be undertaken to improve the process. [ABSTRACT FROM AUTHOR]