Towards Data Markets in Renewable Energy Forecasting

Gonçalves, Carla; Pinson, Pierre; Bessa, Ricardo J.

doi:10.1109/tste.2020.3009615

Cited by 49 publications

(22 citation statements)

References 25 publications

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“…Indeed, some agents contribute to improving the competitors' forecast without having a benefit to their own forecasting accuracy. Then, even if privacy is ensured, such agents can be unwilling to collaborate, which motivates data monetization through data markets [29].…”

Section: Numerical Resultsmentioning

confidence: 99%

Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

Gonçalves

Bessa

2021

IEEE Trans. Sustain. Energy

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Data exchange between multiple renewable energy power plant owners can lead to an improvement in forecast skill thanks to the spatio-temporal dependencies in time series data. However, owing to business competitive factors, these different owners might be unwilling to share their data. In order to tackle this privacy issue, this paper formulates a novel privacypreserving 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 covariance matrix. Besides, asynchronous communication between peers is addressed in the model fitting, and two different collaborative schemes are considered: centralized and peer-topeer. The results for a solar energy dataset show that the proposed method is robust to privacy breaches and communication failures, and delivers a forecast skill comparable to a model without privacy protection.

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Section: Numerical Resultsmentioning

confidence: 99%

Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

Gonçalves

Bessa

2021

IEEE Trans. Sustain. Energy

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“…In [18], a distributed learning framework for exchanging res-related forecast data between agents has been developed, in which the data privacy is preserved and therefore the agents are willing to share their data. In [19], a data marketplace for renewable forecasting has been proposed that allocates a reward to the data providers, proportional to the forecasting accuracy reflected by the reduction in the electricity market imbalance costs.…”

Section: Introductionmentioning

confidence: 99%

Prediction Markets for Probabilistic Forecasting of Renewable Energy Sources

Shamsi

Cuffe

2022

IEEE Trans. Sustain. Energy

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This paper demonstrates how a binary prediction market is capable of achieving a probabilistic renewable energy forecast. In prediction markets, participants trade shares associated with the outcome of unknown future events (here, the renewable production, as a random variable), and the instantaneous price of shares represents the probability of the outcome. The focus of this study is to exploit this informational value of the prediction market price in renewable energy forecasting. To this end, in this paper three different methods of renewable probabilistic forecasting have been considered as the trading agents in a binary prediction market, the aggregated probability of the renewable output is elicited from the equilibrium price in this market and finally, the full cumulative distribution function of possible renewable output is extracted through regression analysis. The proposed method is applied to the test cases of three onshore wind farms in Australia. The simulation results suggest that the performance of the proposed method is superior to the individual models and forecasting is improved in terms of reduction in the electricity market imbalance costs.

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“…We also consider unilateral data sharing where agents can assess the value of data and can trade data for money. An example of this paradigm arises in the case of renewable energy forecasts (Gonçalves et al, 2020). Here we build on the seminal work of Shapley and Shubik (1971), and study conditions that allow existence of competitive prices in these markets which also implement social-welfare-maximizing sharing.…”

Section: Introductionmentioning

confidence: 99%

Data Sharing Markets

Rasouli,

Jordan

2021

Preprint

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With the growing use of distributed machine learning techniques, there is a growing need for data markets that allows agents to share data with each other. Nevertheless data has unique features that separates it from other commodities including replicability, cost of sharing, and ability to distort. We study a setup where each agent can be both buyer and seller of data. For this setup, we consider two cases: bilateral data exchange (trading data with data) and unilateral data exchange (trading data with money). We model bilateral sharing as a network formation game and show the existence of strongly stable outcome under the top agents property by allowing limited complementarity. We propose ordered match algorithm which can find the stable outcome in O(N 2 ) (N is the number of agents). For the unilateral sharing, under the assumption of additive cost structure, we construct competitive prices that can implement any social welfare maximizing outcome. Finally for this setup when agents have private information, we propose mixed-VCG mechanism which uses zero cost data distortion of data sharing with its isolated impact to achieve budget balance while truthfully implementing socially optimal outcomes to the exact level of budget imbalance of standard VCG mechanisms. Mixed-VCG uses data distortions as data money for this purpose. We further relax zero cost data distortion assumption by proposing distorted-mixed-VCG. We also extend our model and results to data sharing via incremental inquiries and differential privacy costs.Preprint. Under review.

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Towards Data Markets in Renewable Energy Forecasting

Cited by 49 publications

References 25 publications

Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

Prediction Markets for Probabilistic Forecasting of Renewable Energy Sources

Data Sharing Markets

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