Pith. sign in

REVIEW 2 cited by

A Joint Energy and Differentially-Private Smart Meter Data Market

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2412.07688 v1 pith:HZX4SX3T submitted 2024-12-10 eess.SY cs.GTcs.SYecon.GNq-fin.ECq-fin.PMq-fin.TR

A Joint Energy and Differentially-Private Smart Meter Data Market

classification eess.SY cs.GTcs.SYecon.GNq-fin.ECq-fin.PMq-fin.TR
keywords dataenergymarketmarketsmetersmartjointproblem
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Given the vital role that smart meter data could play in handling uncertainty in energy markets, data markets have been proposed as a means to enable increased data access. However, most extant literature considers energy markets and data markets separately, which ignores the interdependence between them. In addition, existing data market frameworks rely on a trusted entity to clear the market. This paper proposes a joint energy and data market focusing on the day-ahead retailer energy procurement problem with uncertain demand. The retailer can purchase differentially-private smart meter data from consumers to reduce uncertainty. The problem is modelled as an integrated forecasting and optimisation problem providing a means of valuing data directly rather than valuing forecasts or forecast accuracy. Value is determined by the Wasserstein distance, enabling privacy to be preserved during the valuation and procurement process. The value of joint energy and data clearing is highlighted through numerical case studies using both synthetic and real smart meter data.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Efficient Decentralized Multi-task Dataset Valuation via Model Merging

    cs.CL 2026-07 conditional novelty 7.0

    Task-arithmetic model merging approximates multi-task coalition utilities well enough to recover Dataset Shapley rankings privately and without retraining.

  2. Privacy, Informed Consent and the Demand for Anonymisation of Smart Meter Data

    cs.CY 2025-08 accept novelty 7.0

    Consumers in Great Britain are willing to pay for anonymised smart meter data sharing, and information about privacy risks increases their reluctance to share non-anonymised data.