AI-Powered Decentralized Energy Trading Platform with BiLSTM-based Generation Prediction and Carbon Footprint Analysis
Bibliographic record
Abstract
The increasing complicacy of centralized power trading systems exposes them to risks such as price manipulation and cyberattacks. In contrast, the decentralized and immutable characteristics of blockchain technology present a promising solution to increase the integrity and efficiency of these systems. This paper proposes a decentralized energy trading platform that integrates machine learning (ML) for energy generation prediction and a carbon footprint calculator. The blockchain component is implemented on the Ethereum platform, utilizing smart contracts written in Solidity, with testing conducted via Ganache. The energy generation forecasting leverages a Bidirectional Long Short-Term Memory (BiLSTM) model, which illustrates superior accuracy and minimal validation loss compared to Stacked LSTM and Vanilla LSTM models. LSTMs are recognized as effective tools for predicting energy generation due to their capability to capture temporal dependencies in data. To obtain real-time weather data, the Weatherstack API is employed, allowing for accurate energy generation predictions based on current conditions. The model is trained on two datasets including energy production and weather data from five cities in Spain. Additionally, the platform features a carbon footprint calculator that calculates CO2 emissions associated with various energy purchases, comparing them against emissions from fossil fuel consumption. The integration of decentralized energy trading, accurate energy generation forecasting, and a carbon footprint assessment tool concludes in a comprehensive platform hosted locally using React. This innovative approach not only improves transparency and security in energy transactions but also promotes sustainability by informing users about their environmental impact.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".