Time Series Forecasting of Wind Power using LSTM and GRU Deep Learning Models
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866901673552642048 |
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| author | Pawar, Khushboo Saraswat, Devdas |
| author_facet | Pawar, Khushboo Saraswat, Devdas |
| contents | <p>Abstract— To get the most power out of wind, keep the whole thing stable, and include energysources that are renewable, you need to be able to predict wind power accurately. This study examines the construction and evaluation of machine learning algorithms for forecasting wind power utilising historical meteorological data. We used the WIND POWER collection from Kaggle, which has 6,574 daily records, to keep track of how wind speed, temperatures,precipitation, and environmental indices changed over the course of a year and over the course of a season. A structured strategy was used to make sure that data preprocessing, inquiry examination, feature creation, and strict model validation all worked together to make sure that the results were correct and reliable. The LSTM, GRU, while our own Hybrid LSTM-1DCNN are the three models we have. The Hybrid LSTM-1DCNN did better than both LSTM and GRU in every way. LSTM, on the other hand, was the most accurate and had the best recall.</p> <p>The hybrid model got an F1-score of 98.8%, with an accuracy of 99.1%, a precision of 98.2%, a recall of 97.4%, and a minimal mean round error (MSE) of 0.036.This exemplifies the efficacy of the hybrid model's use of convolutional layers for short-term temporal characteristics and LSTM layers for long-term dependency modelling. Research comparing it to CNN-LSTM and multi-model LSTM frameworks further demonstrated its superiority. The findings highlight the usefulness of hybrid deep learning systems in renewable energy prediction. In the future, researchers may be able to refine this method by including more climate variables and real-timedata into data-driven energy systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19596119 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Time Series Forecasting of Wind Power using LSTM and GRU Deep Learning Models Pawar, Khushboo Saraswat, Devdas Wind power forecasting, time series, Power generation, renewable energy, grid stability, prediction models and Forecasting. <p>Abstract— To get the most power out of wind, keep the whole thing stable, and include energysources that are renewable, you need to be able to predict wind power accurately. This study examines the construction and evaluation of machine learning algorithms for forecasting wind power utilising historical meteorological data. We used the WIND POWER collection from Kaggle, which has 6,574 daily records, to keep track of how wind speed, temperatures,precipitation, and environmental indices changed over the course of a year and over the course of a season. A structured strategy was used to make sure that data preprocessing, inquiry examination, feature creation, and strict model validation all worked together to make sure that the results were correct and reliable. The LSTM, GRU, while our own Hybrid LSTM-1DCNN are the three models we have. The Hybrid LSTM-1DCNN did better than both LSTM and GRU in every way. LSTM, on the other hand, was the most accurate and had the best recall.</p> <p>The hybrid model got an F1-score of 98.8%, with an accuracy of 99.1%, a precision of 98.2%, a recall of 97.4%, and a minimal mean round error (MSE) of 0.036.This exemplifies the efficacy of the hybrid model's use of convolutional layers for short-term temporal characteristics and LSTM layers for long-term dependency modelling. Research comparing it to CNN-LSTM and multi-model LSTM frameworks further demonstrated its superiority. The findings highlight the usefulness of hybrid deep learning systems in renewable energy prediction. In the future, researchers may be able to refine this method by including more climate variables and real-timedata into data-driven energy systems.</p> |
| title | Time Series Forecasting of Wind Power using LSTM and GRU Deep Learning Models |
| topic | Wind power forecasting, time series, Power generation, renewable energy, grid stability, prediction models and Forecasting. |
| url | https://doi.org/10.5281/zenodo.19596119 |