Abstract
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
The deployment of smart metering technologies in the low voltage (LV) grid created conditions for the application of data-driven monitoring and control functions. However, data privacy regulation and consumers' aversion to data sharing may compromise data exchange between utility and customers. This work presents a data-driven method, based on smart meter data, to estimate linear sensitivity factors for three-phase unbalanced LV grids, which combines a privacy-preserving protocol and varying coefficients linear regression. The proposed method enables centralized and peer-to-peer learning of the sensitivity factors. Potential applications for the sensitivity factors are demonstrated by solving voltage violations or computing operating envelopes in a LV grid without resorting to its network topology or electrical parameters.

Abstract
This paper presents a data market aimed at trading energy forecasts data. The system architecture is built using blockchain as a service, allowing access to data streams and establishing a distributed settlement between stakeholders. Energy Forecasts data is presented as the commodity traded in the market, whose settlement is provided through the blockchain on the basis of the extracted value provided by market stakeholders. Our proposal allows market stakeholders to acquire energy forecasts and pay according to the data accuracy, solving the confidentiality problem of freely sharing data. A data quality reward is introduced, steering the compensation sent to market participants. The data market design is presented and an evaluation campaign is performed, showing that the data market produced functionally valid results in comparison with the results achieved with a central simulated approach. Moreover, results show that the data market architecture is able to scale.

Abstract
Geographically distributed wind turbines, photovoltaic panels and sensors (e.g., pyranometers) produce large volumes of data that can be used to improve renewable energy sources (RES) forecasting skill. However, data owners may be unwilling to share their data, even if privacy is ensured, due to a form of prisoner's dilemma: all could benefit from data sharing, but in practice no one is willing to do do. Our proposal hence consists of a data marketplace, to incentivize collaboration between different data owners through the monetization of data. We adapt here an existing auction mechanism to the case of RES forecasting data. It accommodates the temporal nature of the data, i.e., lagged time-series act as covariates and models are updated continuously using a sliding window. A test case with wind energy data is presented to illustrate and assess the effectiveness of such data markets. All agents (or data owners) are shown to benefit in terms of higher revenue resulting from the combination of electricity and data markets. The results support the idea that data markets can be a viable solution to promote data exchange between RES agents and contribute to reducing system imbalance costs.

Abstract
Cooperation between different data owners may lead to an improvement in forecast quality-for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.