Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

Abstract

As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.

A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning

“…Huntingford et al [ 21 ] demonstrated the use of ML in identifying teleconnection, simply complex feedback as well as diagnosing, analysing, or visualise earth system models used to better predict and understand CC impacts For instance, ML may aid teleconnection identification, where complex feedbacks make characterisation difficult from direct equation analysis or visualisation of measurements and Earth System Model (ESM) diagnostics. Other notable studies that resulted in breakthroughs in the analysis of CC and its impacts on biodiversity [ 22 , 23 ], floods [ 24 , 25 ], rainfall [ 26 , 27 ], as well as buildings energy [ 28 , 29 ], and physical infrastructures [ 30 , 31 ], among others. The review of literature has highlighted the importance of ML in CC studies and the need to better understand its impact on humanity and the environment.…”

One-day ahead forecasting of energy production from run-of-river hydroelectric power plants with a deep learning approach

“…In our study, four statistical error criteria comprising mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R) are used for assessment of the goodness of a model to estimate an observed output variable. Their calculation methods are as follows [51][52][53]:…”

Hydropower production prediction using artificial neural networks: an Ecuadorian application case

“…They also used data from several years and rainfall data (aggregated from several basins) as the exogenous variable. Another study developed in South Korea performs a similar analysis considering climate change scenarios and using MLPs [26]. Again, our paper considers more recent techniques aimed explicitly at time series processing, and the comparison with GMDH can be a prospective direction for future work.…”