Smart4RES Webinar Series – Season 1Towards a new Standard for the entire RES forecasting value chain

Webinar #2 Extracting value from data sharing for RES forecasting: Privacy aspects & data monetization

The second episode of the Smart4RES Webinar series will take place on 17 December. In order to give you a taste of the topics that will be addressed, check out this short interview of Ricardo Bessa, Senior Researcher and Coordinator of the Center for Power and Energy Systems at INESC TEC.

Q1: In a decentralized framework where agents share local information generated from diverse sources such as RES power plants, meteorological stations, etc., which method do you propose to optimize collaborative forecasting while preserving privacy?
R.B.: In Smart4RES we are developing a novel privacy-preserving framework that combines data transformation techniques with the alternating direction method of multipliers. This approach allows not only to estimate the model in a distributed fashion but also to protect data privacy, coefficients, and the covariance matrix. Besides, asynchronous communication between peers is addressed both in the model fitting and forecast phases, and two different collaborative schemes are considered: centralized and peer-to-peer. The empirical evaluation using solar data show that collaboration reduces prediction error for most agents, especially under the peer-to-peer approach.

Q2: Can you explain why your concept of data markets for RES forecasting is an essential tool for stakeholders who dispose of valuable data streams?
R.B.: Data sharing between different owners has a high potential to improve RES forecasting skill in different time horizons (e.g., hours-ahead, day-ahead) and consequently the revenue from electricity market players. However, economic incentives, trough data monetization, are fundamental to implement collaborative forecasting schemes since RES agents can be competitors, and therefore unwilling to share their confidential data without benefits. This data market should operate in a way that, after some iterations, agents realize which data is relevant to improve its gain, so that sellers are paid according to their data. The preliminary results in Smart4RES show that data markets can be a solution to foster data exchange between RES agents and contribute to reduce imbalance costs in the electricity market.

Date: 17 December from 14:00 to 15:00, CET.