Short-term wind power forecasting using artificial neural networks-based ensemble model
| dc.contributor.advisor | Folly, Komla | |
| dc.contributor.author | Chen,Qin | |
| dc.date.accessioned | 2022-08-17T11:55:11Z | |
| dc.date.available | 2022-08-17T11:55:11Z | |
| dc.date.issued | 2020 | |
| dc.date.updated | 2022-07-20T10:24:55Z | |
| dc.description.abstract | Short-term wind power forecasting is crucial for the efficient operation of power systems with high wind power penetration. Many forecasting approaches have been developed in the past to forecast short-term wind power. In recent years, artificial neural network-based approaches (ANNs) have been one of the most effective and popular approaches for short-term wind power forecasting because of the availability of large amounts of historical data and strong computational power. Although ANNs usually perform well for short-term wind power forecasting, further improvement can be obtained by selecting suitable input features, model parameters, and using forecasting techniques like spatial correlation and ensemble for ANNs. In this research, the effect of input features, model parameters, spatial correlation and ensemble techniques on short-term wind power forecasting performance of the ANNs models was evaluated. Pearson correlation coefficients between wind speed and other meteorological variables, together with a basic ANN model, were used to determine the impact of different input features on the forecasting performance of the ANNs. The effect of training sample resolution and training sample size on the forecasting performance was also investigated. To separately investigate the impact of the number of hidden layers and the number of hidden neurons on short-term wind power forecasting and to keep a single variable for each experiment, the same number of hidden neurons was used in each hidden layer. The ANNs with a total of 20 hidden neurons are shown to be sufficient for the nonlinear multivariate wind power forecasting problems faced in this dissertation. The ANNs with two hidden layers performed better than the one with a single hidden layer because additional hidden layer adds nonlinearity to the model. However, the ANNs with more than two hidden layers have the same or worse forecasting performance than the one with two hidden layers. ANNs with too many hidden layers and hidden neurons can overfit the training data. Spatial correlation technique was used to include meteorological variables from highly correlated neighbouring stations as input features to provide more surrounding information to the ANNs. The advantages of input features, model parameters, and spatial correlation and ensemble techniques were combined to form an ANN-based ensemble model to further enhance the forecasting performance from an individual ANN model. The simulation results show that all the available meteorological variables have different levels of impact on forecasting performance. Wind speed has the most significant impact on both short-term wind speed and wind power forecasting, whereas air temperature, barometric pressure, and air density have the smallest effects. The ANNs perform better with a higher data resolution and a significantly larger training sample size. However, one requires more computational power and a longer training time to train the model with a higher data resolution and a larger training sample size. Using the meteorological variables from highly related neighbouring stations do significantly improve the forecasting accuracy of target stations. It is shown that an ANNs-based ensemble model can further enhance the forecasting performance of an individual ANN by obtaining a large amount of surrounding meteorological information in parallel without encountering the overfitting issue faced by a single ANN model. | |
| dc.identifier.apacitation | (2020). <i>Short-term wind power forecasting using artificial neural networks-based ensemble model</i>. (). ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/36690 | en_ZA |
| dc.identifier.chicagocitation | . <i>"Short-term wind power forecasting using artificial neural networks-based ensemble model."</i> ., ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2020. http://hdl.handle.net/11427/36690 | en_ZA |
| dc.identifier.citation | 2020. Short-term wind power forecasting using artificial neural networks-based ensemble model. . ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. http://hdl.handle.net/11427/36690 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Chen,Qin AB - Short-term wind power forecasting is crucial for the efficient operation of power systems with high wind power penetration. Many forecasting approaches have been developed in the past to forecast short-term wind power. In recent years, artificial neural network-based approaches (ANNs) have been one of the most effective and popular approaches for short-term wind power forecasting because of the availability of large amounts of historical data and strong computational power. Although ANNs usually perform well for short-term wind power forecasting, further improvement can be obtained by selecting suitable input features, model parameters, and using forecasting techniques like spatial correlation and ensemble for ANNs. In this research, the effect of input features, model parameters, spatial correlation and ensemble techniques on short-term wind power forecasting performance of the ANNs models was evaluated. Pearson correlation coefficients between wind speed and other meteorological variables, together with a basic ANN model, were used to determine the impact of different input features on the forecasting performance of the ANNs. The effect of training sample resolution and training sample size on the forecasting performance was also investigated. To separately investigate the impact of the number of hidden layers and the number of hidden neurons on short-term wind power forecasting and to keep a single variable for each experiment, the same number of hidden neurons was used in each hidden layer. The ANNs with a total of 20 hidden neurons are shown to be sufficient for the nonlinear multivariate wind power forecasting problems faced in this dissertation. The ANNs with two hidden layers performed better than the one with a single hidden layer because additional hidden layer adds nonlinearity to the model. However, the ANNs with more than two hidden layers have the same or worse forecasting performance than the one with two hidden layers. ANNs with too many hidden layers and hidden neurons can overfit the training data. Spatial correlation technique was used to include meteorological variables from highly correlated neighbouring stations as input features to provide more surrounding information to the ANNs. The advantages of input features, model parameters, and spatial correlation and ensemble techniques were combined to form an ANN-based ensemble model to further enhance the forecasting performance from an individual ANN model. The simulation results show that all the available meteorological variables have different levels of impact on forecasting performance. Wind speed has the most significant impact on both short-term wind speed and wind power forecasting, whereas air temperature, barometric pressure, and air density have the smallest effects. The ANNs perform better with a higher data resolution and a significantly larger training sample size. However, one requires more computational power and a longer training time to train the model with a higher data resolution and a larger training sample size. Using the meteorological variables from highly related neighbouring stations do significantly improve the forecasting accuracy of target stations. It is shown that an ANNs-based ensemble model can further enhance the forecasting performance of an individual ANN by obtaining a large amount of surrounding meteorological information in parallel without encountering the overfitting issue faced by a single ANN model. DA - 2020 DB - OpenUCT DP - University of Cape Town KW - Artificial intelligence KW - artificial neural networks KW - ensemble model KW - particle swarm optimization LK - https://open.uct.ac.za PY - 2020 T1 - Short-term wind power forecasting using artificial neural networks-based ensemble model TI - Short-term wind power forecasting using artificial neural networks-based ensemble model UR - http://hdl.handle.net/11427/36690 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/36690 | |
| dc.identifier.vancouvercitation | . Short-term wind power forecasting using artificial neural networks-based ensemble model. []. ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2020 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/36690 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Electrical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.subject | Artificial intelligence | |
| dc.subject | artificial neural networks | |
| dc.subject | ensemble model | |
| dc.subject | particle swarm optimization | |
| dc.title | Short-term wind power forecasting using artificial neural networks-based ensemble model | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |