Budget-constrained Collaborative Renewable Energy Forecasting Market

2025-01-21

AI Summary

Addressing challenges in renewable energy power forecasting—including fragmented data ownership across multi-source spatiotemporal datasets, insufficient incentives for data sharing, and tight budget constraints—this paper proposes a collaborative data market tailored for wind and solar power forecasting. Method: We introduce an interpretable spline-based LASSO regression model for forecasting; design a budget-constrained two-sided bidding mechanism; and develop a redundancy-avoiding temporal feature pricing-and-allocation algorithm integrating game-theoretic principles with constrained optimization. Contribution/Results: This work is the first to deeply couple data governance, economic incentive design, and predictive modeling. Empirical evaluation demonstrates over 10% reduction in average RMSE for wind power forecasting, while data providers achieve substantial economic returns. The study further validates a positive synergy between enhanced forecasting accuracy and the commercial sustainability of the data market.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data integration and cleaning

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.

Problem

• Renewable Energy Data Integration
• Prediction Accuracy Improvement
• Fair Compensation for Data Providers

Innovation

• Spline LASSO Regression
• Equitable Bidding Rules
• Incentive Mechanism with Price Caps