Solar Energy Harvesting Dataset (2023–2025)
Fuente:
Zenodo
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Recurso digital |
| Langue: | anglais |
| Publié: |
Zenodo
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866901566403903488 |
|---|---|
| author | Abubakar, John Amanesi Dede, Jens Förster, Anna |
| author_facet | Abubakar, John Amanesi Dede, Jens Förster, Anna |
| contents | <p>This dataset contains over 2.5 million real-world measurements collected from a photovoltaic (PV) inverter system between 2023 and 2025. The data were recorded as part of research on solar energy forecasting, energy-aware intermittent computing, and predictive checkpointing for IoT nodes.</p> <p>Measurements were acquired from a SH10RT inverter system equipped with dual MPPT channels and three-phase output monitoring. Each record includes synchronized current and voltage readings captured at high temporal resolution, supporting studies in energy forecasting, digital twins, and low-power system optimization.</p> <p>The dataset is divided into two major components merged by timestamp:<br><strong>Current dataset</strong> — MPPT and phase current readings<br><strong>Voltage dataset</strong> — MPPT, phase, and battery voltage readings</p> <p><strong>File Structure</strong><br>- `solar_data_2023_2025.csv.gz` — full merged dataset (compressed)<br>- `sample_10k.csv` — 10,000-row subset for testing and analysis<br>- All timestamps are stored as UTC datetime converted from nanosecond epoch.</p> <p><strong>Applications</strong><br>- Solar energy forecasting (short-term and day-ahead)<br>- Energy-aware and intermittent system modeling<br>- Battery and inverter performance analytics<br>- Edge intelligence and digital twin simulation</p> <p><strong>Citation</strong><br>If you use this dataset, please cite:<br>> Abubakar, J. A. (2025) et al. <em>Solar Energy Harvesting Dataset (2023–2025)</em>. Zenodo. https://doi.org/10.5281/zenodo.17400949</p> <p><strong>License</strong><br>Released under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. This allows for free use, redistribution, and adaptation with appropriate credit.</p> <p><strong>Acknowledgment</strong><br>Data collected within the research framework on predictive energy-aware computing and solar-powered IoT systems, University of Bremen.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17400949 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Solar Energy Harvesting Dataset (2023–2025) Abubakar, John Amanesi Dede, Jens Förster, Anna solar energy photovoltaic IoT intermittent computing edge intelligence smart grid energy forecasting MPPT time series <p>This dataset contains over 2.5 million real-world measurements collected from a photovoltaic (PV) inverter system between 2023 and 2025. The data were recorded as part of research on solar energy forecasting, energy-aware intermittent computing, and predictive checkpointing for IoT nodes.</p> <p>Measurements were acquired from a SH10RT inverter system equipped with dual MPPT channels and three-phase output monitoring. Each record includes synchronized current and voltage readings captured at high temporal resolution, supporting studies in energy forecasting, digital twins, and low-power system optimization.</p> <p>The dataset is divided into two major components merged by timestamp:<br><strong>Current dataset</strong> — MPPT and phase current readings<br><strong>Voltage dataset</strong> — MPPT, phase, and battery voltage readings</p> <p><strong>File Structure</strong><br>- `solar_data_2023_2025.csv.gz` — full merged dataset (compressed)<br>- `sample_10k.csv` — 10,000-row subset for testing and analysis<br>- All timestamps are stored as UTC datetime converted from nanosecond epoch.</p> <p><strong>Applications</strong><br>- Solar energy forecasting (short-term and day-ahead)<br>- Energy-aware and intermittent system modeling<br>- Battery and inverter performance analytics<br>- Edge intelligence and digital twin simulation</p> <p><strong>Citation</strong><br>If you use this dataset, please cite:<br>> Abubakar, J. A. (2025) et al. <em>Solar Energy Harvesting Dataset (2023–2025)</em>. Zenodo. https://doi.org/10.5281/zenodo.17400949</p> <p><strong>License</strong><br>Released under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. This allows for free use, redistribution, and adaptation with appropriate credit.</p> <p><strong>Acknowledgment</strong><br>Data collected within the research framework on predictive energy-aware computing and solar-powered IoT systems, University of Bremen.</p> |
| title | Solar Energy Harvesting Dataset (2023–2025) |
| topic | solar energy photovoltaic IoT intermittent computing edge intelligence smart grid energy forecasting MPPT time series |
| url | https://doi.org/10.5281/zenodo.17400949 |