IoT and machine learning models for multivariate very short‐term time series solar power forecasting
Abstract In solar energy generation, the inherent variability caused by cloud cover and weather events often leads to sudden fluctuations in power outputs. Addressing this challenge, the authors’ study focuses on very short‐term solar irradiance (SI) prediction. Leveraging multivariate time series d...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Wireless Sensor Systems |
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| Online Access: | https://doi.org/10.1049/wss2.12088 |
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| author | Su Kyi Attaphongse Taparugssanagorn |
| author_facet | Su Kyi Attaphongse Taparugssanagorn |
| author_sort | Su Kyi |
| collection | DOAJ |
| description | Abstract In solar energy generation, the inherent variability caused by cloud cover and weather events often leads to sudden fluctuations in power outputs. Addressing this challenge, the authors’ study focuses on very short‐term solar irradiance (SI) prediction. Leveraging multivariate time series datasets, the authors improve very short‐term SI predictions. To achieve accurate very short‐term SI predictions, the authors employ machine learning techniques throughout the forecasting process. Additionally, the authors’ work pioneers the integration of the Internet of Things (IoT) into solar power systems, a novel approach in the field. The authors leverage LoRa (long range) technology for low‐cost, low‐power, and long‐range wireless control networks. The authors’ study focuses on SI forecasting using long short‐term memory and bi‐directional long short‐term memory (Bi‐LSTM) models, achieving high accuracy. The SI forecasts are evaluated in terms of root‐mean‐square error (RMSE) and mean absolute error in relation to meteorological data and sky image data. The improvement in performance can be attributed to the Bi‐LSTM's bidirectional nature, allowing it to incorporate future information during training, thereby enhancing its predictive capabilities. Overall, the results suggest that the Bi‐LSTM model is more suitable for accurately forecasting SI, particularly in scenarios requiring short‐term predictions based on rapidly changing environmental factors. |
| format | Article |
| id | doaj-art-a1d43f6fdff04b448c6a234bc4955279 |
| institution | Kabale University |
| issn | 2043-6386 2043-6394 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Wireless Sensor Systems |
| spelling | doaj-art-a1d43f6fdff04b448c6a234bc49552792024-12-23T18:42:04ZengWileyIET Wireless Sensor Systems2043-63862043-63942024-12-0114638139510.1049/wss2.12088IoT and machine learning models for multivariate very short‐term time series solar power forecastingSu Kyi0Attaphongse Taparugssanagorn1ICT Department School of Engineering and Technology IoT Systems Engineering Asian Institute of Technology Pathum Thani ThailandICT Department School of Engineering and Technology IoT Systems Engineering Asian Institute of Technology Pathum Thani ThailandAbstract In solar energy generation, the inherent variability caused by cloud cover and weather events often leads to sudden fluctuations in power outputs. Addressing this challenge, the authors’ study focuses on very short‐term solar irradiance (SI) prediction. Leveraging multivariate time series datasets, the authors improve very short‐term SI predictions. To achieve accurate very short‐term SI predictions, the authors employ machine learning techniques throughout the forecasting process. Additionally, the authors’ work pioneers the integration of the Internet of Things (IoT) into solar power systems, a novel approach in the field. The authors leverage LoRa (long range) technology for low‐cost, low‐power, and long‐range wireless control networks. The authors’ study focuses on SI forecasting using long short‐term memory and bi‐directional long short‐term memory (Bi‐LSTM) models, achieving high accuracy. The SI forecasts are evaluated in terms of root‐mean‐square error (RMSE) and mean absolute error in relation to meteorological data and sky image data. The improvement in performance can be attributed to the Bi‐LSTM's bidirectional nature, allowing it to incorporate future information during training, thereby enhancing its predictive capabilities. Overall, the results suggest that the Bi‐LSTM model is more suitable for accurately forecasting SI, particularly in scenarios requiring short‐term predictions based on rapidly changing environmental factors.https://doi.org/10.1049/wss2.12088internet of thingslearning (artificial intelligence)sensorssolar power |
| spellingShingle | Su Kyi Attaphongse Taparugssanagorn IoT and machine learning models for multivariate very short‐term time series solar power forecasting IET Wireless Sensor Systems internet of things learning (artificial intelligence) sensors solar power |
| title | IoT and machine learning models for multivariate very short‐term time series solar power forecasting |
| title_full | IoT and machine learning models for multivariate very short‐term time series solar power forecasting |
| title_fullStr | IoT and machine learning models for multivariate very short‐term time series solar power forecasting |
| title_full_unstemmed | IoT and machine learning models for multivariate very short‐term time series solar power forecasting |
| title_short | IoT and machine learning models for multivariate very short‐term time series solar power forecasting |
| title_sort | iot and machine learning models for multivariate very short term time series solar power forecasting |
| topic | internet of things learning (artificial intelligence) sensors solar power |
| url | https://doi.org/10.1049/wss2.12088 |
| work_keys_str_mv | AT sukyi iotandmachinelearningmodelsformultivariateveryshorttermtimeseriessolarpowerforecasting AT attaphongsetaparugssanagorn iotandmachinelearningmodelsformultivariateveryshorttermtimeseriessolarpowerforecasting |