An Intelligent Vehicle-to-Building Energy Trading System Using Transfer Learning and Blockchain
Bibliographic record
Abstract
The rapid development of the internet of electric vehicles (IoEV) and the advancement of electric vehicle (EV) charging technology are transforming energy management for both residential and commercial users. Vehicle-to-building (V2B) energy trading is emerging as a groundbreaking approach that incorporates the exchange of energy between EVs and buildings. Despite the fact that V2B energy trading is able to reduce energy costs, main-grid complexity, and greenhouse gas emissions, it faces challenges when it comes to cooperative decision-making, resource efficient computation, and user transaction security. To address these challenges, this study aims to enhance energy exchange efficiency and dynamic energy interactions with enhanced security in urban environments. With these objectives, this paper proposes a novel energy trading method that integrates transfer learning (TL) and blockchain technology. TL makes it possible to adapt the knowledge gathered from vehicle-to-vehicle (V2V) systems to V2B settings, which reduces the computational resources required and boosts the overall efficiency. Blockchain technology, on the other hand, provides a secure framework for transaction verification while granting users enhanced control over their data privacy. We demonstrate the effectiveness of our proposed technique using detailed simulations conducted with real-world data. Our simulation results indicate that the proposed approach improves the convergence speed by around 50% compared to training from scratch, while buildings achieve up to 38% higher profits relative to scenarios without our proposed strategy. We also simulate the system model using the Ethereum blockchain platform to determine its real-world feasibility. These experiments demonstrate that the system has the potential to facilitate efficient energy trading to ensure user security and economically beneficial transactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".