Effective artificial neural network-based wind power generation and load demand forecasting for optimum energy management
TL;DR: In this paper , an artificial neural network-based paradigm was proposed to predict wind power generation and load demand, where the meteorological parameters, including wind speed, temperature, and atmospheric pressure, were fed to the model as inputs.
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Abstract: The variability of power production from renewable energy sources (RESs) presents serious challenges in energy management (EM) and power system stability. Power forecasting plays a crucial role in optimal EM and grid security. Then, accurate power forecasting ensures optimum scheduling and EM. Therefore, this study proposes an artificial neural network- (ANN-) based paradigm to predict wind power (WP) generation and load demand, where the meteorological parameters, including wind speed, temperature, and atmospheric pressure, are fed to the model as inputs. The normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) criteria are used to evaluate the forecasting technique. The performance of ANN was compared to four machine learning methods: LASSO, decision tree (DT), regression vector machines (RVM), and kernel ridge regression (KRR). The obtained results show that ANN provides high effectiveness and accuracy for WP forecasting. Furthermore, ANN has proven to be an interesting tool in ensuring optimum scheduling and EM.
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Citations
Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects
TL;DR: A review of current advances and prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques is presented in this article , where different approaches and models have been used for renewable energy forecasting and discusses their strengths and limitations.
A review of the applications of artificial intelligence in renewable energy systems: An approach-based study
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Wind speed short-term prediction using recurrent neural network GRU model and stationary wavelet transform GRU hybrid model
D.G. Fantini,R.N. Silva,M.B.B. Siqueira,M.S.S. Pinto,M. Guimarães,A.C.P. Brasil +5 more
TL;DR: This study evaluates the application of wavelet transform as a pre-processing technique for Recurrent Neural Networks (RNNs) to enhance hourly wind forecasting accuracy using a Gated Recurrent Unit (GRU) model, but finds no significant improvement.
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Forecasting Renewable Energy Generation with Machine learning and Deep Learning: Current Advances and Future Prospects
27 Mar 2023
TL;DR: A review of current advances and future prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques is presented in this paper , where different approaches and models have been used for renewable energy forecasting and discusses their strengths and limitations.
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