Applied-Information Technology in Short-Term Wind Speed Forecast Model for Wind Farms Based on Ant Colony Optimization and BP Neural Network

Abstract

To improve the short-term wind speed forecasting accuracy of wind farms, a prediction model based on back propagation (BP) neural network combining ant colony algorithm is built to predict short-term wind speed. The input variables of BP neural network predictive model are historical wind speeds, temperature, and air pressure. Ant colony algorithm is used to optimize the weights and bias of BP neural networks. Using the ant colony optimization BP neural network model to predict the future 1h wind speed, the simulation results show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.

An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation

“…The above weaknesses can be introduced to partly explain that the advantages of PDA‐ANN reconstruction over other PDA‐based reconstructions are not as substantial as we had expected. However, the above limitations can be overcome if the ANN model is trained in combination with some sophisticated, intelligent algorithms such as genetic algorithm (Sexton et al, ), particle swarm optimization (Mirjalili et al, ), and ant colony optimization (Zhao et al, ). In this way, the performance of ANN would be further improved by using intelligent algorithms to search the best weights and thresholds of ANN structure.…”