Akademik Veri Yönetim Sistemi
Energy Sources, Part B: Economics, Planning and Policy, cilt.19, sa.1, 2024 (SCI-Expanded)
Realistic energy production and consumption projections are needed by national governments that are developing policies to reduce greenhouse gas emissions and ensure energy supply security. In this study, four types of artificial neural network (ANN) models–including a back propagation (BP) model, ANN-BP, and three Rao algorithm models, namely ANN-Rao_1, ANN_Rao2 and ANN-Rao_3 were developed to determine hydroelectric power (HEP) generation projections for Brazil, Russia, India, China, South Africa, and Turkey (BRICS-T) countries. Gross domestic product (GDP), population, import, and export data were inputted as independent variables into the models. Data from the period of 1990‒2014 were used for ANN model development. Data from the period of 2015‒2020 were used as reference data to test the models’ predictive power. According to the error values calculated for the training and test sets, the ANN-Rao_3 model predicted HEP generation values for BRICS-T countries more accurately than the other models. The results obtained from this study predict that the HEP generation value of the BRICS-T countries will increase by 28.1% by 2040. According to the RMSE values obtained for the test datasets, compared to the BP algorithm, the Rao_3 algorithm increased ANN performance by 34.1% for Brazil, 24.1% for Russia, 3% for India, 13% for South Africa, and 12.1% for Turkey. The total HEP generation value of the BRICS-T countries in 2040 was projected to be 2815.69 TWh. Current BRICS-T countries’ HEP investments are not sufficient to achieve their renewable energy targets. In light of this research, relevant policy implications may be derived.