无线电通信技术2024，Vol.50Issue(4)：771-778,8.DOI:10.3969/j.issn.1003-3114.2024.04.020
基于ARIMA-PSO-LSTM的太阳能预测
Solar Intensity Prediction Based on ARIMA-PSO-LSTM Model
摘要
Abstract
Solar energy is one emerging renewable energy source,which can be converted into electricity for the use of wireless Sen-sor Networks(WSN),and prediction of solar energy can effectively use energy to save energy consumption and maintain continuous and stable operation of network.In this paper,a new combined energy prediction model is proposed to predict solar radiation intensity,in which an improved algorithm Particle Swarm Optimization(PSO)is introduced to find optimal parameters of a Long Short Term Memory(LSTM)model.Auto-Regressive Integrated Moving Average(ARIMA)is initially employed to distill and forecast linear elements of solar radiation data.Secondly,PSO is used to optimize hyperparameters of the LSTM model,which helps to improve the accuracy and robust-ness of the model prediction.Then,the optimized LSTM model is used to predict nonlinear components in the data.Finally,forecast out-comes of both models are combined.Experiments show that the new combined model has higher prediction accuracy than ARIMA,LSTM and other models.关键词
自回归差分移动平均模型/长短期记忆神经网络模型/粒子群优化算法/能量预测算法Key words
ARIMA model/LSTM neural network model/PSO algorithm/energy prediction algorithm分类
信息技术与安全科学引用本文复制引用
沈露露,黄晋浩,花敏,周雯..基于ARIMA-PSO-LSTM的太阳能预测[J].无线电通信技术,2024,50(4):771-778,8.基金项目
国家自然科学基金(61801225)National Natural Science Foundation of China(61801225) （61801225）
