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Advances in Applied Energy is a peer-reviewed journal published by Elsevier, covering Integrated Energy Systems Optimization, Smart Grid Energy Management, Building Energy and Comfort Optimization.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Advances in Applied Energy 12 (2023) 45–58.
Formats any DOI in Advances in Applied Energy style. No sign-up. |
| Publishes research in | Integrated Energy Systems Optimization Smart Grid Energy Management Building Energy and Comfort Optimization Electric Vehicles and Infrastructure Hybrid Renewable Energy Systems |
| ISSN | 2666-7924 |
| Citation impact (2-yr) | 14.08 |
| h-index | 63 |
| i10-index | 206 |
| Total citations | 12,833 |
| Article processing charge | $3,600 |
| Open access | Yes |
| Top institutions publishing here | Lawrence Berkeley National Laboratory |
| Journal website | www.journals.elsevier.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
The high environmental impacts of transport mean that there is an increasing interest in utilising low-carbon alternative energy carriers and powertrains within the sector. While electricity has been mooted as the energy carrier of choice for passenger vehicles, as the mass and range of the vehicle increases, electrification becomes more difficult. This paper reviews the…
As one of the most important and advanced technology for carbon-mitigation in the building sector, building performance simulation (BPS) has played an increasingly important role with the powerful support of building energy modelling (BEM) technology for energy-efficient designs, operations, and retrofitting of buildings. Owing to its deep integration of multi-disciplinary approaches, the researchers, as well…
Machine learning has been widely adopted for improving building energy efficiency and flexibility in the past decade owing to the ever-increasing availability of massive building operational data. However, it is challenging for end-users to understand and trust machine learning models because of their black-box nature. To this end, the interpretability of machine learning models has…
Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the…
The importing of renewable energy will be one part of the process of defossilizing the energy systems of countries and regions, which are currently heavily dependent on the import of fossil-based energy carriers. This study investigates the possibility of importing renewable methanol comprised of hydrogen and carbon dioxide. Based on a methanol synthesis simulation model,…
