Hydroelectric Generation Forecasting with Long Short Term Memory (LSTM) Based Deep Learning Model for Turkey

TL;DR

This study develops and evaluates an LSTM-based deep learning model to accurately forecast Turkey's monthly hydroelectricity production over long periods, demonstrating the effectiveness of using extensive historical data for reliable predictions.

Contribution

The paper introduces a long-term hydroelectricity forecasting model using a 100-layer LSTM network trained on over 10 years of data, achieving high accuracy in Turkey.

Findings

The 100-layer LSTM model with 120 months of data achieved a MAPE of 13.1%.

Using at least 120 months of historical data improves long-term hydroelectricity forecasts.

Deep learning models can effectively predict hydroelectric generation with sufficient historical data.

Abstract

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.