Abstract
Due to the low dispatchability of wind power, the massive integration of this energy source in electrical systems requires short-term and very short-term wind farm power output forecasting models to be as efficient and stable as possible. A study is conducted in the present paper of potential improvements to the performance of artificial neural network (ANN) models in terms of efficiency and stability. Generally, current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station, in addition to selecting a fixed number of time periods prior to the forecasting. In this respect, new ANN models are proposed in this paper, which are developed by: varying the number of prior 1-h periods (periods prior to the prediction hour) chosen for the input layer parameters; and/or incorporating in the input layers data from a second weather station in addition to the wind farm reference station. It has been found that the model performance is always improved when data from a second weather station are incorporated. The mean absolute relative error (MARE) of the new models is reduced by up to 7.5%. Furthermore, the longer the forecast horizon, the greater the degree of improvement.
Highlights
A major impediment to the large-scale integration of wind power in electrical systems is the low dispatchability of this energy source
The study developed in the present paper focuses on models which employ the technique of artificial neural networks (ANNs) to forecast wind farm power production [21, 22, 26], [27, 29–31, 33, 34]
A series of interesting conclusions can be drawn from the results of this study with respect to possible improvements in the performance of ANN models for the short-term forecasting of wind farm power output
Summary
A major impediment to the large-scale integration of wind power in electrical systems is the low dispatchability of this energy source. A strategy that can be employed to improve wind energy integration in electrical systems is to optimize the performance of short-term forecasting models of wind farm power production. This strategy is the focus of the present study. Any mismatches that might arise between supply and demand forecasting are subsequently corrected on the day Theory of Complexity - Definitions, Models, and Applications itself [6–9]. The mismatch correction as the result of imprecise forecasting entails additional costs for the electrical system [7, 10]. These extra costs are generally absorbed by the end user and/or electricity producer, with the latter burdened by an additional production cost
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
