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Accurate power forecasting from renewable energy sources (RES) is crucial for integrating additional RES capacity into the power system and realizing sustainability goals. This work emphasizes the importance of integrating decentralized spatio-temporal data into forecasting models. However, decentralized data ownership presents a critical obstacle to the success of such spatio-temporal models, and incentive mechanisms to foster data-sharing need to be considered. The main contributions are a) a comparative analysis of the forecasting models, advocating for efficient and interpretable spline LASSO regression models, and b) a bidding mechanism within the data/analytics market to ensure fair compensation for data providers and enable both buyers and sellers to express their data price requirements. Furthermore, an incentive mechanism for time series forecasting is proposed, effectively incorporating price constraints and preventing redundant feature allocation. Results show significant accuracy improvements and potential monetary gains for data sellers. For wind power data, an average root mean squared error improvement of over 10% was achieved by comparing forecasts generated by the proposal with locally generated ones.

Abstract
Establishing the relationship between online communication channels and the stages of the consumer decision-making process was the aim of this research. A review of online communication channels and consumer behavior models was conducted for this purpose. Subsequently, three experiments (car glass repair, retail of clothing and sports equipment, and retail of books and technological products) were carried out that consisted of questionnaires that were applied in companies belonging to different industries. The questionnaires were used to measure the expectations and preferences of consumers at each stage for each channel. The results showed that there is no relationship between online communication channels and the stages of the decision-making process. They also revealed a gap between expectations and preferences for each channel in each phase. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.