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

by   Mehmet Bulut, et al.

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 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.


page 1

page 2

page 3

page 4


Short-term daily precipitation forecasting with seasonally-integrated autoencoder

Short-term precipitation forecasting is essential for planning of human ...

A Deep Learning Model for Heterogeneous Dataset Analysis – Application to Winter Wheat Crop Yield Prediction

Western countries rely heavily on wheat, and yield prediction is crucial...

Predicting malaria dynamics in Burundi using deep Learning Models

Malaria continues to be a major public health problem on the African con...

Predicting Disease Progress with Imprecise Lab Test Results

In existing deep learning methods, almost all loss functions assume that...

SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation

Our ability to synthesize sensory data that preserves specific statistic...

Advanced Statistical Learning on Short Term Load Process Forecasting

Short Term Load Forecast (STLF) is necessary for effective scheduling, o...

Advanced Deep Regression Models for Forecasting Time Series Oil Production

Global oil demand is rapidly increasing and is expected to reach 106.3 m...

Please sign up or login with your details

Forgot password? Click here to reset