Given the growing installed capacity, wind energy will exert a profound impact on the flexibility of modern energy systems. Wind power forecasting is a practical solution for dealing with the attributed variations and uncertainties, balancing supply and demand, and improving the reliability of the system. To achieve more accurate and generalizable forecast models, comprehensive data sets, supplied by multiple wind farms owing to their spatio-temporal dependencies, are required. In addition, data aggregation/collaboration across many wind farms scattered around a country is difficult, if not impossible, due to complex administrative processes, industry competition, and data privacy and security concerns. This article offers federated learning-based wind energy forecasting as a novel decentralized collaborative modeling method capable of training a single model on data from many wind farms without jeopardizing the privacy or security of data. To this end, rather than sending private data across sites, local model parameters are securely transmitted. A comparison between the proposed private distributed model and non-private centralized and fully private localized models indicates the high performance of the proposed federated learning-based wind power forecasting with 87.96% accuracy. Enjoying the smoothing effect, the higher generalizability of the proposed model with 83.63% accuracy is also substantiated in comparison to localized and centralized approaches while the privacy of the underlying data is preserved.

Carbon emissions significantly contribute to global warming, amplifying the occurrence of extreme weather events and negatively impacting the overall environmental transformation. In line with the global commitment to combat climate change through the Paris Agreement (COP21), the European Union (EU) has formulated strategies aimed at achieving climate neutrality by 2050. To achieve this goal, EU member states focus on developing long-term national strategies (NLTSs) and implementing local plans to reduce greenhouse gas (GHG) emissions in alignment with EU objectives. This study focuses on the case of Sweden and aims to introduce a comprehensive data-driven framework that predicts CO₂ emissions by using a diverse range of input features. Considering the scarcity of data points, we present a refined variation of multi-task learning (MTL) called weighted multi-task learning (WMTL). The findings demonstrate the superior performance of the WMTL model in terms of accuracy, robustness, and computation cost of training compared to both the basic model and MTL model. The WMTL model achieved an average mean squared error (MSE) of 0.12 across folds, thus outperforming the MTL model’s 0.15 MSE and the basic model’s 0.21 MSE. Furthermore, the computational cost of training the new model is only 20% of the cost required by the other two models. The findings from the interpretation of the WMTL model indicate that it is a promising tool for developing data-driven decision-support tools to identify strategic actions with substantial impacts on the mitigation of CO₂ emissions.