基于长短期记忆（LSTM）深度学习模型的土耳其水力发电量预测
Hydroelectric Generation Forecasting with Long Short Term Memory (LSTM) Based Deep Learning Model for Turkey
AI总结：
本研究针对土耳其水力发电量预测问题，构建基于LSTM的深度学习模型，通过不同时间跨度数据与模型层数的对比实验，确定最优参数配置，为长期水电发电规划提供支撑。
摘要：
水力发电是可再生能源的一种，在土耳其已应用多年。基于水库的水力发电厂发电量会因不同参数而变化，因此，水力发电量的估算对发电规划而言具有重要意义。本文采用基于长短期记忆（LSTM）网络的深度学习模型，对土耳其的月度水力发电量进行估算。所设计的深度学习模型基于多年水力发电时间序列与未来发电规划构建。研究利用实际发电量数据与不同的LSTM深度学习模型，检验了它们在预测下一年度月度水力发电量方面的性能。结果表明，将基于多年实际发电量数据的时间序列与深度学习模型结合使用，在长期预测中效果良好。研究发现，根据RMSE（均方根误差）和MAPE（平均绝对百分比误差）值，采用120个月（10年）水力发电时间数据的100层LSTM模型预测精度最高，其年度总MAPE值为0.1311（13.1%），月平均分布MAPE值为1.09%。在该模型相关实验中，采用144个月（12年）水力发电时间数据的100层LSTM模型取得了最优结果，年度RMSE值为29689，月度分布RMSE值为2474.08。根据研究结果，建议使用至少覆盖120个月发电量的时间数据，以构建可接受的LSTM水力发电预测模型。
英文摘要：
Hydroelectricity is one of the renewable energy source, has been used for many years in Turkey. The production of hydraulic power plants based on water reservoirs varies based on different parameters. For this reason, the estimation of hydraulic production gains importance in terms of the planning of electricity generation. In this article, the estimation of Turkey's monthly hydroelectricity production has been made with the long-short-term memory (LSTM) network-based deep learning model. The designed deep learning model is based on hydraulic production time series and future production planning for many years. By using real production data and different LSTM deep learning models, their performance on the monthly forecast of hydraulic electricity generation of the next year has been examined. The obtained results showed that the use of time series based on real production data for many years and deep learning model together is successful in long-term prediction. In the study, it is seen that the 100-layer LSTM model, in which 120 months (10 years) hydroelectric generation time data are used according to the RMSE and MAPE values, are the highest model in terms of estimation accuracy, with a MAPE value of 0.1311 (13.1%) in the annual total and 1.09% as the monthly average distribution. In this model, the best results were obtained for the 100-layer LSTM model, in which the time data of 144 months (12 years) hydroelectric generation data are used, with a RMSE value of 29,689 annually and 2474.08 in monthly distribution. According to the results of the study, time data covering at least 120 months of production is recommended to create an acceptable hydropower forecasting model with LSTM.
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