A Solar Altitude Angle Model for Efficient Solar Energy Predictions
Sunlight is one of the most frequently used ambient energy sources for energy harvesting in wireless sensor networks. Although virtually unlimited, solar radiation experiences significant variations depending on the weather, the season, and the time of day, so solar-powered nodes commonly employ sol...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/5/1391 |
| _version_ | 1811305360286285824 |
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| author | Sergio Herrería-Alonso Andrés Suárez-González Miguel Rodríguez-Pérez Raúl F. Rodríguez-Rubio Cándido López-García |
| author_facet | Sergio Herrería-Alonso Andrés Suárez-González Miguel Rodríguez-Pérez Raúl F. Rodríguez-Rubio Cándido López-García |
| author_sort | Sergio Herrería-Alonso |
| collection | DOAJ |
| description | Sunlight is one of the most frequently used ambient energy sources for energy harvesting in wireless sensor networks. Although virtually unlimited, solar radiation experiences significant variations depending on the weather, the season, and the time of day, so solar-powered nodes commonly employ solar prediction models to effectively adapt their energy demands to harvesting dynamics. We present in this paper a novel energy prediction model that makes use of the altitude angle of the sun at different times of day to predict future solar energy availability. Unlike most of the state-of-the-art predictors that use past energy observations to make predictions, our model does not require one to maintain local energy harvesting patterns of past days. Performance evaluation shows that our scheme is able to provide accurate predictions for arbitrary forecasting horizons by performing just a few low complexity operations. Moreover, our proposal is extremely simple to set up since it does not require any particular tuning for each different scenario or location. |
| first_indexed | 2024-04-13T08:25:08Z |
| format | Article |
| id | doaj.art-e6d5b35e7afa4c85918f99b0bd242c17 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:25:08Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e6d5b35e7afa4c85918f99b0bd242c172022-12-22T02:54:32ZengMDPI AGSensors1424-82202020-03-01205139110.3390/s20051391s20051391A Solar Altitude Angle Model for Efficient Solar Energy PredictionsSergio Herrería-Alonso0Andrés Suárez-González1Miguel Rodríguez-Pérez2Raúl F. Rodríguez-Rubio3Cándido López-García4Department of Telematics Engineering, University of Vigo, 36310 Vigo, SpainDepartment of Telematics Engineering, University of Vigo, 36310 Vigo, SpainDepartment of Telematics Engineering, University of Vigo, 36310 Vigo, SpainDepartment of Telematics Engineering, University of Vigo, 36310 Vigo, SpainDepartment of Telematics Engineering, University of Vigo, 36310 Vigo, SpainSunlight is one of the most frequently used ambient energy sources for energy harvesting in wireless sensor networks. Although virtually unlimited, solar radiation experiences significant variations depending on the weather, the season, and the time of day, so solar-powered nodes commonly employ solar prediction models to effectively adapt their energy demands to harvesting dynamics. We present in this paper a novel energy prediction model that makes use of the altitude angle of the sun at different times of day to predict future solar energy availability. Unlike most of the state-of-the-art predictors that use past energy observations to make predictions, our model does not require one to maintain local energy harvesting patterns of past days. Performance evaluation shows that our scheme is able to provide accurate predictions for arbitrary forecasting horizons by performing just a few low complexity operations. Moreover, our proposal is extremely simple to set up since it does not require any particular tuning for each different scenario or location.https://www.mdpi.com/1424-8220/20/5/1391energy harvestingsolar energyenergy managementenergy prediction |
| spellingShingle | Sergio Herrería-Alonso Andrés Suárez-González Miguel Rodríguez-Pérez Raúl F. Rodríguez-Rubio Cándido López-García A Solar Altitude Angle Model for Efficient Solar Energy Predictions Sensors energy harvesting solar energy energy management energy prediction |
| title | A Solar Altitude Angle Model for Efficient Solar Energy Predictions |
| title_full | A Solar Altitude Angle Model for Efficient Solar Energy Predictions |
| title_fullStr | A Solar Altitude Angle Model for Efficient Solar Energy Predictions |
| title_full_unstemmed | A Solar Altitude Angle Model for Efficient Solar Energy Predictions |
| title_short | A Solar Altitude Angle Model for Efficient Solar Energy Predictions |
| title_sort | solar altitude angle model for efficient solar energy predictions |
| topic | energy harvesting solar energy energy management energy prediction |
| url | https://www.mdpi.com/1424-8220/20/5/1391 |
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