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A Comparison of Hour-Ahead Solar Irradiance Forecasting Models Based on LSTM Network

机译:基于LSTM网络的小时前太阳辐照度预报模型比较

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摘要

The intermittence and fluctuation character of solar irradiance places severe limitations on most of its applications. The precise forecast of solar irradiance is the critical factor in predicting the output power of a photovoltaic power generation system. In the present study, Model I-A and Model II-B based on traditional long short-term memory (LSTM) are discussed, and the effects of different parameters are investigated; meanwhile, Model II-AC, Model II-AD, Model II-BC, and Model II-BD based on a novel LSTM-MLP structure with two-branch input are proposed for hour-ahead solar irradiance prediction. Different lagging time parameters and different main input and auxiliary input parameters have been discussed and analyzed. The proposed method is verified on real data over 5 years. The experimental results demonstrate that Model II-BD shows the best performance because it considers the weather information of the next moment, the root mean square error (RMSE) is 62.1618 W/m(2), the normalized root mean square error (nRMSE) is 32.2702, and the forecast skill (FS) is 0.4477. The proposed algorithm is 19.19 more accurate than the backpropagation neural network (BPNN) in terms of RMSE.
机译:太阳辐照度的间歇性和波动性对其大多数应用造成了严重限制。太阳辐照度的精确预报是预测光伏发电系统输出功率的关键因素。本研究讨论了基于传统长短期记忆(LSTM)的模型I-A和模型II-B,并研究了不同参数的影响;同时,提出了基于新型LSTM-MLP结构的II-AC型、II-AD型、II-BC型和II-BD型,用于一小时前的太阳辐照度预测。对不同的滞后时间参数和不同的主输入和辅助输入参数进行了讨论和分析。所提方法在5年以上的真实数据上进行了验证。实验结果表明,模型II-BD考虑了次时刻的天气信息,其均方根误差(RMSE)为62.1618 W/m(2),归一化均方根误差(nRMSE)为32.2702%,预报技能(FS)为0.4477。该算法在RMSE方面比反向传播神经网络(BPNN)的准确率高19.19%。

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    Yunnan Normal Univ, Solar Energy Res Inst, Kunming 650500, Yunnan, Peoples R China|Yunnan Normal Univ, Sch Phys & Elect Informat, Kunming 650500, Yunnan, Peoples R China|Yunnan Key Lab Optoelect Informat Technol, Kunming 650500, Yunnan, Peoples R China;

    Yunnan Normal Univ, Sch Phys & Elect Informat, Kunming 650500, Yunnan, Peoples R China|Yunnan Key Lab Optoelect Informat Technol, Kunming 650500, Yunnan, Peoples R China;

    Yunnan Normal Univ, Solar Energy Res Inst, Kunming 650500, Yunnan, Peoples R ChinaYunnan Normal Univ, Sch Phys & Elect Informat, Kunming 650500, Yunnan, Peoples R China;

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