Smart, Connected, and Sustainable: The Transformation of Maritime Ports Through Electrification, IoT, 5G, and Green Energy
Bibliographic record
Abstract
In recent years, there has been a fast expansion in the usage of renewable energy sources (RESs) in power distribution systems. Numerous advantages result from this advancement, such as environmental friendliness, cost-effective power generation, easier maintenance, and energy sustainability and reliability. Reducing reliance on fossil fuels, which are of significant environmental concern, and increasing energy efficiency are two benefits of integrating RESs into maritime systems, such as port microgrids. As a result, ports are implementing several programs to increase energy efficiency using various RESs that are supported by power electronic converters. To highlight the most recent developments in seaport electrification and infrastructure, this work conducts a systematic review. It addresses important issues like energy efficiency enhancements, environmental concerns, the integration of renewable energy sources, the Internet of Things (IoT), and regulatory and legal compliance. The study also discusses technology strategies like digitization, electrification, onshore power supply systems, and port energy storage options. Operational tactics, including peak-shaving methods and energy-efficient operations, are also covered. Additionally, an infrastructure framework—which includes port microgrids and smart seaport microgrids—that is intended to enhance energy efficiency in contemporary ports is examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".