Residual adaptive input normalization for forecasting renewable energy generation in multiple countries

Being able to accurately predict the generation of Renewable Energy Sources (RES), such as photovoltaic and wind generation, can provide valuable information for various stakeholders, including grid operator, energy producers, and consumers. Deep Learning (DL) provided powerful tools for this goal. However, it is not trivial to leverage data from multiple countries to improve forecasting accuracy due to distribution shift phenomena that are often involved. Adaptive normalization approaches have recently emerged as an effective tool for tackling such difficulties. These formulations, despite their very promising results in classification tasks, often face challenges in (auto)regressive tasks, especially when combined with recent powerful DL architectures. To overcome this limitation we introduce a novel residual-based adaptive normalization layer that is capable of re-introducing the information discarded during the normalization process to the forecasting model. The proposed method enables the use of data that are generated by different distributions but expresses the same phenomenon, allowing for exploiting additional data sources, e.g., multiple countries, when training DL models, leading to significant improvements in forecasting accuracy.