Browsing by Author "Hailemichael Kebede"

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    Deep Learning Models for Future Hydropower Production Prediction: a Case Study of the Koka Hydroelectric Power Plant, Ethiopia
    (Addis Ababa University, 2024-06) Ababa Lata; Hailemichael Kebede
    Hydropower, a clean and renewable energy source driven by the water cycle, plays a crucial role in many countries. Predicting future hydropower production is vital for strategic decision-making and optimizing energy resource utilization. This study evaluated three deep learning models (Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gate Recurrent Unit (GRU)) for daily and weekly hydropower energy production future prediction at the Koka Dam in Ethiopia’s Awash basin. The models aimed to forecast not only daily and weekly production but also the next year of generation. Daily and weekly hydropower production, precipitation, minimum temperature, maximum temperature, relative humidity, maximum wind speed, minimum wind speed, wind direction, and all sky-surface short wave data from September 2010 to November 2023 were used. After preprocessing, the data was split into training and testing sets for model training and evaluation respectively. Performance metrics like R-squared, MAE, MSE, and RMSE were calculated for each model. The GRU model emerged as the best performer, achieving an R-squared value of 0.9920, MAE of 8.6121, MSE of 143.5549, and RMSE of 11.9814 for daily hydropower energy generation prediction, and R-squared of 0.9960, MAE of 5.9925, MSE of 66.2187, and RMSE of 8.1375 for weekly hydropower energy generation prediction. This superior model was then employed to predict the Koka Dam’s daily and weekly hydropower energy production for the next 1 year. The study identified the right hydro-power production prediction model for future potential. Furthermore, this outcome can help to maximize the use of medium hydro-power resources, contributing to the region’s energy security and sustainable development.
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    The Effect of External Finance on Economic Growth in Ethiopia: An Autoregressive Distributed Lag Modelling Approach
    (Addis Ababa University, 2022-06) Hailemichael Kebede; Sisay Debebe (PhD)
    External financial sources are considered as one of the very important accelerators of economic growth. Development requires economic growth to alleviate poverty, and external financial sources are perceived as a necessary condition for more rapid growth. A descriptive and quantitative research approaches were conducted with the aim to examine the overall effects of external sources of finance on economic growth of Ethiopia. An Autoregressive Distributed Lag (ARDL) model was used to estimate the short and long run relationship of variables using time series data sets from 1990-2021 G.C. The results shown that external debt and grant have a positive and statistically significant while remittance and foreign direct investment have a positive but not statistically significant effect on real gross domestic product in the long-run. In the short-run, remittance and grant have positive but statistically not significant effect on rGDP at their current values. External debt has a negative and statistically not significant effect on rGDP at its current value. FDI has a negative and significant effect on rGDP both at its current and lagged value. From the empirical results it can be concluded that external finances support the process of economic growth hence there by support the process of poverty reduction in Ethiopia in the long-run. Absorptive capacity, institutional and bureaucratic quality should be improved to alleviate the inefficiencies of external finances in economic growth in the short-run and maximize the benefits earned from external financial resources. Key words: external finance, rGDP, remittance, grant, external debt, FDI, ARDL