南方电网技术2024，Vol.18Issue(11)：97-105,9.DOI:10.13648/j.cnki.issn1674-0629.2024.11.011
基于相似日分析和改进鲸鱼算法优化LSTM网络模型的光伏功率短期预测
Short-Term Prediction of Photovoltaic Power Based on Similar Day Analysis and Improved Whale Algorithm to Optimize LSTM Network Model
摘要
Abstract
In order to solve the constraints of many factors such as ambient temperature,wind speed and solar irradiance on photovoltaic power generation prediction,a long short-term memory(LSTM)neural network model based on similar day analysis and improved whale algorithm optimization to realize short-term prediction of photovoltaic power is proposed.Firstly,the Pearson correlation coefficient is used for feature selection to remove meteorological characteristics that are not correlated with the output power of photovoltaics.Secondly,according to the actual situation that the power generation of photovoltaic power plants is close under similar meteorological conditions,gray relation analysis(GRA)is used to select dates similar to the meteorological characteristics of the forecast day as the training set.Then,an improved whale algorithm(IWOA)is proposed to optimize the hyperparameters of LSTM deep neural network to minimize the root mean square error of the prediction model.Finally,the historical data of photovoltaic power generation of Yulara Desert No.3 photovoltaic power station in Australia is used as experimental data,and the GRA-IWOA-LSTM neural network model is used to make predictions.The simulation results show that the prediction results of the GRA-IWOA-LSTM model are more accurate than the prediction effects of other models under different weather types.关键词
相似日/光伏功率短期预测/灰色关联分析/改进鲸鱼优化算法/长短期记忆神经网络Key words
similar day/short-term prediction of photovoltaic power/grey relation analysis/improved whale optimization algorithm/long short-term neural network分类
信息技术与安全科学引用本文复制引用
薛阳,李金星,杨江天,李清,丁凯..基于相似日分析和改进鲸鱼算法优化LSTM网络模型的光伏功率短期预测[J].南方电网技术,2024,18(11):97-105,9.基金项目
国家自然科学基金资助项目(52075316) （52075316）
上海市2021年度"科技创新行动计划"(21DZ1207502) （21DZ1207502）
国网杭州供电公司科技项目(5211HZ17000F). Supported by the National Natural Science Foundation of China(52075316) （5211HZ17000F）
Shanghai 2021"Science and Technology Innovation Action Plan"(21DZ1207502) （21DZ1207502）
the Science and Technology Project of State Grid Hangzhou Power Supply Company(5211HZ17000F). （5211HZ17000F）
