The Transition Toward Merging Big Data Analytics, IoT, and Artificial Intelligence with Blockchain in Transactive Energy Markets
Bibliographic record
Abstract
Blockchain is a special technology for peer-to-peer (P2P) trading platforms, which uses decentralized storage to record all transaction data. Blockchain was first used in 2009 in the infrastructure of the financial sector of the Bitcoin cryptocurrency. Recently, further applications have evolved in this area to provide decentralized transaction storage by employing innovative mechanisms that enable decentralized operation. This mechanism is called a smart contract that operates based on individually defined rules (e.g., quality, quantity, and price specifications) that enable an independent matching of market players at the distribution level on the supplier side and their potential customers and end-users as well as prosumers. In this article, in addition to introducing the concept of Blockchain, the way this cutting-edge technology deals with different agents in the energy sector and markets and its impact on the operation performance of the electricity and energy industry have been examined. Thus, merging other emerging technologies and platforms, such as big data analysis, the Internet of Things (IoT), and artificial intelligence (AI) with Blockchain, can solve many practical open challenges in smart electrical grids and the energy domain. This article delves into the impacts of integrating these platforms to boost the interoperability between different components and enhance the liquidity of markets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".