We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A NOVEL WIND POWER PREDICTION SCHEME BY COUPLING THE BP NEURAL NETWORK MODEL WITH THE FIREWORKS ALGORITHM.
- Authors
YONGGANG LI; YAOTONG SU; LEI XIA; YONGFU LI; HONG XIANG; QINGLONG LIAO
- Abstract
Wind power has unpredictable, intermittent traits due to meteorological conditions and environmental factors. Large-scale grid integration of wind energy will undoubtedly challenge system stability. This study developed a fireworks algorithmbackpropagation (FWA-BP) neural network model to forecast wind power using wind speed, direction, and power as model inputs. Optimization of the BP network weights and thresholds occurred through the fireworks algorithm. Compared to a standard BP network, the FWA-BP model yielded improved prediction accuracy seen through a lower mean squared error. This implies that the approach introduced in this paper significantly enhances global search capabilities, prediction accuracy, and speed. It contributes to enhancing the reliability of the power system, optimizing resource allocation, and improving wind power scheduling, with substantial potential and economic significance.
- Subjects
WIND power; ARTIFICIAL neural networks; FIREWORKS; WIND forecasting; WIND speed; ALGORITHMS
- Publication
Scalable Computing: Practice & Experience, 2024, Vol 25, Issue 4, p3114
- ISSN
1895-1767
- Publication type
Article
- DOI
10.12694/scpe.v25i4.2974