Abstract
Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as sub-models for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deep-learning-based sub-models. Lastly, variances are obtained from sub-models and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.
Highlights
The demand of renewable energy application is growing stronger over the recent years, in response to increasingly high energy consumption and serious environment pollutions [1]
The proposed wavelet threshold denoising (WTD)-recurrent neural network (RNN)-adaptive neuro fuzzy inference system (ANFIS) model is validated on forecasting wind speed under multi-step-ahead situations, that is, 1-step-ahead (15 min), 2-step-ahead (30 min) and 3-stepahead (45 min)
It is compared with three-layer artificial neural network (ANN), support vector machine (SVM), RNN, WTD-ANN, WTD-SVM and WTD-RNN
Summary
The demand of renewable energy application is growing stronger over the recent years, in response to increasingly high energy consumption and serious environment pollutions [1]. The global renewable energy capacity has exceeded 1800 GW by the end of 2015 [2,3]. Among those energy sources, wind power is a typical kind, which is widely distributed and easy to access and it has become one of the fastest developing sources. The chaotic nature of wind speed is unavoidable, which restricts the application of in-grid wind power. As wind fluctuates arbitrarily and uncertainly, wind power integration will challenge the security and stability of power system operations. An accurate wind speed forecasting is expected for the development of large-scale wind power utilization, which can benefit wind power integrated system
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.