An Intelligent Long Short-Term Memory-Based Machine Learning Model for the Potential Assessment of Global Hydropower Capacity in Sustainable Energy Transition and Security

Abstract

Climate change is a pressing global issue with severe consequences for the planet and human health. The Earth’s temperature has risen by 2 °C from 1901 to 2023, and this warming trend is expected to continue, causing potentially dangerous shifts in climate. Climate change impacts are already visible, with more frequent and severe heat waves, droughts, intense rain, and floods becoming increasingly common. Therefore, hydropower can contribute to addressing the global climate change issue and help to achieve global energy transition and stabilize global energy security. A Long Short-Term Memory (LSTM)-based model implemented in Python for global and regional hydropower forecasting was developed for a study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh in 2030, 2772.06 TWh in 2040, 2811.41 TWh in 2050, and 3195.79 TWh in 2060, as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent State (CIS), Europe, North America, and South and Central America. The global hydropower potential is also expected to increase from 4350.12 TWh in 2023 to 4806.26 TWh in 2030, 5393.80 TWh in 2040, 6003.53 TWh in 2050, and 6644.06 TWh in 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM-based model were found to be 98%. This study will help in the efficient scheduling and management of hydropower resources, reducing uncertainties caused by environmental variability such as precipitation and runoff. The proposed model contributes to the energy transition and security that is needed to meet the global climate targets.

1. Introduction

A basic human requirement that contributes significantly to socioeconomic advancement is energy [1]. The world’s energy demand is fast rising as a result of rapid industrialization and population increase [2]. Because of the excessive consumption of conventional energy resources such as petroleum, coal, and nuclear, their capacity is decreasing due to increased energy demand [3]. Non-renewable fuel also contributes to environmental contamination and disrupts the ecosystem’s balance [4]. Globally, the principal source of electricity generation is fossil fuels, and as a result the power industry is the largest source of carbon emissions [5]. The use of fossil fuels for power and heat generation resulted in the emission of 15.18 billion tons of carbon emissions in 2020 [6]. Aside from carbon emissions, fossil fuel-based power plants emit noise, vibration, and heat, as well as sulfur dioxide (SO₂), nitrogen oxide (NO), carbon mono oxide (CO), and particulate matter [7]. Global warming is caused by carbon emissions in the atmosphere, whereas NOx depletes the ozone layer. SO₂ degrades vegetation and materials, produces acid rain that is harmful to human health, and can impair visibility [8]. Every year, 8.34 million individuals around the globe die as a result of air pollution [9]. Non-conventional renewable energy resources such as hydro, wind, solar, ocean, and geothermal are available globally in abundant quantity and termed as limitless and pollution-free [10]. Due to the depletion of fossil resources and the rising pace of carbon emissions, several governments throughout the world are moving toward green energy sources [11].

Meanwhile, many developing and underdeveloped countries are experiencing an energy crisis owing to insufficient electricity additions to the power infrastructure [12]. Some of the contributing elements to the energy crisis include rapid industrialization, population increase, and a high rate of urbanization [13]. The gross domestic product (GDP) of developing countries has suffered as a result of the energy crisis, which has stalled production and harmed citizens’ social lives [14]. The closure of the industry sector in these countries due to the energy crisis has contributed to unemployment [15]. So, the energy dilemma has now escalated into a global security concern.

Managing global warming in a sustainable way to alleviate energy crises and meet the ever rising energy demand worldwide. Hydropower has the capability to address this issue and yield global energy transition and energy security [16]. Despite transformative changes in the global energy system, as well as uncertainty in costs and geopolitical friction, energy transition and energy security should be achieved by carefully utilizing the high hydropower potential. Hydropower development also offers a comprehensive method for managing water resources for applications such as irrigation, home usage, industrial usage, and flood control [17]. Hydropower has the potential to contribute to a global environmentally friendly and long-term energy mix [18]. The availability of a reliable financial support structure aids in increasing renewable energy generation. When developing hydropower plants, selecting a site is critical since it also manages other services such as flood control and drinking water supply [19].

The generation of energy from a hydroelectric plant is based on simple concepts but the construction of large dams in developing countries has been criticized for causing cost overruns, inflation, and debt, making them very risky projects [20]. High-risk mega dams are not as suitable as small hydropower (SHP) systems, which are more adaptable and can be developed more quickly and readily to address environmental and social problems [21]. With 99% of its electricity coming from SHP, Norway is a great example of how a flexible approach may have significant benefits. SHP plants should be low-head and run-of-the-river technology and have an installed capacity of no more than 10 MW. Also, SHP systems are less expensive than large hydropower projects, which necessitate substantial planning and people relocation [22].

The International Energy Agency (IEA) states that 28 rising and developing economies mostly rely on hydropower to supply their electrical needs [23]. Africa, North America (Canada), and Southeast Asia have an untapped hydropower potential that should be exploited to increase access to low-cost electricity and also to meet their fast-growing energy demand [24]. By 2030, China is expected to continue to be the largest hydropower market globally, contributing to 40% of the growth in capacity [25]. India is also expected to increase access to low-cost hydropower generation by 2026 [26]. Some issues like viable economic sites for hydropower slowed the development in Brazil, Argentina, and Colombia, who had set the targets to lead hydropower generation [27]. However, in Europe, only Turkey will develop the largest hydropower capacity in the coming years [28]. In 2022, 4408 TWh energy was produced globally from hydro with 3.7% increment as of 2021. The installed capacity of hydro was reached at 1397 GW in 2022 with a 2.7% increment as of 2021. SHP plants had an installed capacity of 175 GW in 2022, which is a 6% increase compared to 2021. The complete status of global hydropower capacity is shown in Figure 1 [29].

The global goal is to reduce the carbon component of energy in a way to combat climate change and achieve Sustainable Development Goal 7 (SDG-7) for clean and affordable energy. Hydropower development aims to achieve a reduction in carbon emissions by providing additional financial resources. Despite a number of problems, hydropower remains a top renewable option for achieving green energy targets, with the added benefit of storing water for farming and drinking. Because of the aforementioned prospects and potential benefits of hydropower, developing and underdeveloped countries should place a strong emphasis on the development of hydropower projects with the goal of increasing hydropower’s share in the global energy mix. So, it is vital to assess the full potential of the hydropower resource regionally (Middle East, Africa, Asia, CIS, Europe, North America, South and Central America) and globally, with proper recommendations for achieving energy transition and energy security globally. The following questions are addressed in this work:

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How can an LSTM-based model be implemented in Python (3.12.3 version) to train hydropower systems?

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What is the annual past and current states (1980–2022) and future prospects (2023–2060) of hydropower development and utilization globally?

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How can we achieve the long-term goals of sustainable energy transition and energy security through the optimal utilization of hydropower sources?

2. Materials and Methods

The establishment of a hydropower sector will accelerate global energy transition and stabilize global energy security. Regional and global hydropower potential was forecasted using an LSTM-based model implemented in Python for the study period of 2023 to 2060 by taking the input data from 1980 to 2022. Then, an evaluation of the proposed model was performed using the mean absolute error (MAE), root mean square deviation (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²). Here, a question arises: why is an LSTM-based model superior for this application over other methods such as ARIMA, GRU, Prophet, or classical machine learning approaches (Random Forest, XGBoost)? The answer is briefly described in

Table 1. Comparative analysis of machine learning models.

Aspect	LSTM	ARIMA	GRU	Prophet	Classical Methods (Random Forest and XGBoost)
Handling sequential data [30]	Designed for sequential data; captures long-term dependencies and complex temporal patterns without strict assumptions on data stationarity or linearity	Primarily for univariate, stationary time series; struggles with complex nonlinear patterns	Similar to LSTM but simpler; good for sequences but may underperform LSTM on complex patterns	Handles time series with seasonality and holidays well; less flexible for non-calendar data	Not inherently sequential; requires feature engineering to capture temporal dependencies
Multivariate inputs [31]	Supports multivariate time series and external covariates naturally	Extensions like VAR/ARIMAX are needed for multivariate data; more complex to implement	Supports multivariate inputs similarly to LSTM but often less expressive	Can incorporate external regressors but mainly focused on univariate seasonal data	Can handle multivariate features easily but temporal context must be engineered explicitly
Nonlinearity and complex pattern [32]	Excels at modeling nonlinear, complex, and dynamic temporal dependencies	Assumes linear relationships; limited in capturing complex nonlinearities	Captures nonlinearities but generally less powerful than LSTM	Limited to additive models with trend and seasonality components; less flexible	Can model nonlinearities but temporal dependencies may be missed without careful design
Handling noise and sparsity [33]	Robust to noise with sufficient data and preprocessing; performance degrades with high sparsity	Sensitive to noise and sparsity; performance drops significantly under noisy conditions	Similar noise sensitivity as LSTM; may require tuning	Handles irregular timestamps well but struggles with heavy noise or abrupt changes	Noise robustness depends on feature engineering; no inherent temporal noise handling
Training and computational cost [34]	Computationally intensive; requires more training time and tuning; benefits from large datasets and GPUs	Fast training; less computationally demanding; suitable for smaller datasets	Faster training than LSTM but still requires tuning and computational resources	Efficient and user-friendly; requires less tuning than LSTM or ARIMA	Generally faster training; scalable; but may need complex features for time series
Interpretability [35]	Difficult to interpret; considered a “black box” model	Highly interpretable with clear statistical foundations	More interpretable than LSTM but still complex	Designed for interpretability with clear trend and seasonality components	Variable importance available but temporal reasoning is indirect
Adaptability to non-stationary data [36]	Can learn from non-stationary data without explicit differencing or transformations	Requires stationarity or differencing to handle trends and seasonality	Similar to LSTM in adaptability	Handles trend and seasonality explicitly; less flexible for abrupt changes	Depends on feature engineering; no inherent stationarity assumptions
Multistep forecasting [37]	Well-suited for multistep forecasting with proper training	Multistep forecasting possible but error accumulates over steps	Similar to LSTM but may be less accurate for complex sequences	Automatically generates multistep forecasts but less reliable with irregular trends	Possible but requires recursive or direct strategies; can be complex
Best use case	Large, complex, nonlinear, multivariate, and dynamic time series with sufficient data and computational resources	Small, stationary, linear univariate time series with limited noise	Medium-complexity sequences where faster training is desired	Business time series with clear seasonality and holiday effects, and irregular timestamps	When temporal dependencies are weak or feature engineering is strong; tabular data

. The research flow chart of the proposed model is given in Figure 2.

2.1. Raw Data Collection

Gathering the raw data of hydropower plays a crucial function of optimization, increased performance, and regulatory conduct in its operations. This includes tools and methods for directly controlling and assessing the hydropower systems in terms of requirements and performance. Continuous data enables hydropower plants to evaluate important characteristics, which include water intake, efficiency of the turbines, the amount of power generated, and previous energy intake. In addition, systems at work apply the LSTM model, which analyzes past data, like hydrological, operational, meteorological data, and past energy consumption across each region, including the Middle East, Asia, Africa, CIS, Europe, North America, and South and Central America, and produces the expected energy output, which assists operators in decision-making processes relating to energy production and the utilization of the available hydropower resource in each region. The details for the types of raw data collected for hydropower across each region include the following:

• Annual open source water data was taken from Global Water Monitor Consortium (GWMC), which includes water flow rates, reservoir levels, and rainfall across all regions [38].
• Operational data like turbine performance, output of generators, and efficiency calculations was taken from [39].
• The annual meteorological data of each region like weather-related measures, which can affect water intake schedules, was taken from [40].
• The annual open source past energy consumption data of each region was taken from [41], which was used to predict future energy forecast accurately.

Combining these types of data (hydrological, operational, meteorological, and past energy consumption data) across all regions (Middle East, Asia, Africa, CIS, Europe, North America, South and Central America) into a cohesive analysis framework of an LSTM model is very essential for the accurate prediction of hydropower for accelerating energy transition and stabilizing energy security at global scale. The proposed model of hydropower systems often operates under a set of assumptions that may not fully capture the complexities of real-world conditions. Limitations include the impact of noise on data collection, delayed reporting of hydrological data, and the variability introduced by climate shocks and infrastructure changes. These limitations vary across each region, but the proposed model incorporates uncertainty and stochastic inputs, such as probabilistic rainfall forecasts and real-time monitoring of reservoir levels in each region.

2.2. Data Pre-Processing

Data pre-processing plays an essential role in the process of analyzing and forecasting hydropower production, especially when using an LSTM model. Pre-processing resolves problems such as noise, missing values, and irrelevant features. In the context of hydropower, the raw data is collected from the sensors, and effective pre-processing improves the performance of the LSTM model. In this study, a normalization technique was applied to scale the data to a more uniform range and, thus, enhance model convergence and model accuracy. This is especially important in our case because different features have varying scales such as hydrological, operational, environmental, and meteorological features, as well as the amount of energy consumed in the past. Last but not the least, the LSTM model needs some pre-processing to handle temporal structures properly. The first-order differencing technique is used commonly to make data stationary before using it to train the model.

2.3. Data Splitting

Data splitting comes after the data preprocessing of hydropower modeling. In this step the preprocessed data is divided into train (80%), validation (10%), and test (10%) sets by random sampling techniques. The training data is used to learn the parameters of a model, the validation data is then used to select the parameters, and finally, the test data is used to assess the performance of the model.

2.4. Model Selection and Training for Hydropower Forecasting

The architecture of the LSTM model is shown in Figure 3 [42]. The architecture typically consists of multiple layers, including the Input Layer (accepts the input time series data), LSTM Layers (generally, 1 to 3 LSTM layers are used, with each layer containing 50 to 200 units, depending on the complexity of the data), and Dense Layer (a fully connected layer with 1 to 10 units, often used for regression tasks) [43]. The sequence length or window size is crucial for capturing temporal dependencies. The commonly selected choice range for the proposed study is from 10 to 100 time steps [44]. LSTM layers typically use the default activation function (tanh) for the cell state and sigmoid for the gates, and the dense layer commonly employs Rectified Linear Unit (ReLU) or linear activation functions, depending on whether the task is classification or regression [45]. The choice of loss function is dependent on the regression tasks (mean squared error is commonly used) and classification tasks (categorical crossentropy is preferred). In an LSTM model, the popular optimizer is Adam, which is widely used for its adaptive learning rate capabilities, and stochastic gradient descent is sometimes used for its simplicity and effectiveness in certain scenarios [46]. The learning rate is typically set between 0.001 and 0.01, with adjustments made based on model performance during training. Training is usually conducted over 50 to 200 epochs, with early stopping implemented to prevent overfitting [47]. Hyper-parameter tuning is often performed using techniques such as grid search or random search to optimize the model’s performance. This process involves systematically varying parameters like the number of layers, units, learning rate, and batch size to find the best configuration [48].

The LSTM model was preferred for this study due to its ability to handle complex sequential data. The LSTM model is a type of recurrent neural network (RNN) that can learn long-term dependencies in time series data, making it useful for energy forecasting [49]. Energy forecasting models have the ability to manage trends in energy use and handle complex patterns. Such energy models can forecast future energy demand and consumption [50]. In this study, an LSTM model for energy forecasting using the multivariate and univariate input data is used, along with an encoder–decoder and convolutional neural network (CNN) architecture. The developed model was trained to output a single value or a sequence of values for multi-step forecasting. The LSTM model has the ability to handle the missing data from sequences of data points in energy forecasting. Missing values in the LSTM model can be identified by capturing patterns and data dependencies for accurate energy forecasting [51]. The best practice to train the LSTM model is to organize the data through scaling and normalization; then, the model extracts the important features to optimize the hyperparameters. Further, regularization techniques are implemented using weight decay, which can further prevent overfitting. Finally, the performance of the model can be evaluated using MAE, RMSE, MAPE, and R². Then, the output of the LSTM model for prediction or classification is interpreted by understanding the problem and the type of output the model is generating. Applying techniques like thresholding, scaling, or post-processing can yield meaningful results [52]. The LSTM can be computed by using gates, which include initial gate, missing gate, and final gate; their mathematical description is given in Equation (1) to Equation (5) [53]:

$$G_x=\mathsf{\omega }(A_x{d}_f+B_x{e}_{f-1}+C_x)$$

(1)

$$G_y=\mathsf{\omega }(A_y{d}_f+B_y{e}_{f-1}+C_y)$$

(2)

$$G_z=\mathsf{\omega }(A_z{d}_f+B_z{e}_{f-1}+C_z)$$

(3)

$$Y_f={ U}_x {H_p Y}_{f-1}{+G}_x H_p \mathrm{t}\mathrm{a}\mathrm{n}\mathrm{e} (A_x{d}_f+B_x{e}_{f-1}+C_x)$$

(4)

$$d_{f-1}=G_x{H}_p \mathrm{t}\mathrm{a}\mathrm{n}\mathrm{e} \left(Y_f\right)$$

(5)

$A_{x }{and B}_x$ are weight matrices; C is the bias values of the corresponding gates, $H_p$ is Hadamrd’s product, $\mathsf{\omega }$ is considered a sigmoid function, and $d_f and e_{f-1}$ represent the hidden and cell states. Equation (1) represents the initial gate value; missing values in the operation can be identified by Equation (2), while the data undergo an operation as shown in Equation (3). The input data undergoes gradation using Equation (4) and the final output is obtained through Equation (5) [53]. Then, MAE, RMSE, MAPE, and R² are used to evaluate the performance of the LSTM model that predicts hydropower generation and water flow.

2.5. Model Evaluation

MAE gives the arithmetic mean of errors in a set of forecasts with no distinction between positive and negative errors. RMSE involves summing the squared differences of actual minus predicted values, dividing by the number of observations multiplied by the actual values, and taking the square root of the answer [54]. MAPE permits the prediction accuracy to be expressed in terms of an absolute percentage error relative to the observed sale values. In the case of R², it assess the extent to which the variation in the dependent variable can be predicted by the independent variables in the given model [55]. The error between actual and predicted values is measured by MAE and obtained by Equation (6), the deviation between actual and predicted values is measured by RMSE and is obtained by Equation (7), the error in predicted value is indicated by MAPE and is obtained by Equation (8), and finally, model forecasting accuracy is obtained by the correlation density, which is indicated by R² and is obtained by Equation (9) [56].

$$\mathrm{M}\mathrm{A}\mathrm{E}={\displaystyle \frac{1}{N}}\sum _{k=1}^n[v_{predicted}-v_{exact}]$$

(6)

$$RMSE=\sqrt{{\displaystyle \frac{1}{N}}\sum _{k=1}^n[v_{predicted}-v_{exact}}{]}^2$$

(7)

$$\mathrm{M}\mathrm{A}\mathrm{P}\mathrm{E}={\displaystyle \frac{1}{N}}\sum _{k=1}^n\left[{\displaystyle \frac{\left(v_{predicted}-v_{real}\right)}{v_{real}}}\right]\times 100\%$$

(8)

$${\mathrm{R}}^2=1-{\displaystyle \frac{{ {\sum }_{k=1}^n \left(v_{predicted}-v_{predicted}\right)}^2}{{ {\sum }_{k=1}^n \left({ǔ}_{real}-v_{real}\right)}^2}}$$

(9)

${ǔ}_{real}$ in Equation (9) is considered to be the average of actual power generation data. The LSTM model is considered to be the most accurate tool for energy forecasting, but this tool has some limitations, which include computational expense and a limited ability to handle non-sequential inputs. However, in HVAC systems, the ultra-short-term energy consumption in buildings is forecasted by an attention-based LSTM model, which has substantial advantages over conventional machine learning algorithms [57]. Short- to medium- and long-term energy forecasting with dependencies on historical data and pattern can be performed by implementing the LSTM model. In this particular application, the model has crossed support vector regression (SVR), artificial neural network (ANN), and RNN [58]. Further, the concept of hybrid models is introduced by combining LSTM with other techniques like ARIMA, SARIMA, Exponential Smoothing, Prophet, Holt Winter, and SARIMAX, which would be feasible for enhancing the accuracies of the model. Real-world applications were tested using hybrid models that produce novel results with better performance.

3. Results and Discussions

Hydropower is an environmentally friendly source of energy that is used to produce approximately 20% of the world’s energy needs. It has become a major energy source in many countries. The world’s hydropower supplies over 50% of national electricity, and more than 65 countries derive more than 50% of their electricity from hydropower and 32 countries derive over 80%. The scope for further growth is enormous, particularly in Africa and Asia, which contain much of the economically exploitable hydropower resource. Hydropower can be a critical path in the process of development, mainly for developing countries. Hydropower is central in the future economic planning of nations like Sudan, Rwanda, and Bhutan. Accessibility to a stable electric power source contributes to industrialization, uplifts quality of life, and enhances access to service delivery such as education and health services. Hydropower has many environmental benefits over the use of fossil products. Soon, therefore, the application of hydropower can offer long-term impacts for fighting climate change because it can supply a reliable electricity source to complement fluctuating renewable energies such as wind and solar power. Irrigation, flood control, and improvements in water supply security can be offered by the development of large dams for generating hydroelectricity. This multipurpose approach makes hydropower specially valuable in integrated water resources management. Hydropower technology has improved a bit in the past century, especially with a high energy conversion efficiency, which is at an average of 91%. Hence, in this study, regional and global hydropower potentials were forecasted using an LSTM model for the study period of 2023 to 2060 by taking the input data from 1980 to 2022. Further, an evaluation of the LSTM model was performed using MAE, RMSE, MAPE, and R².

Hydropower forecasting results are presented by regions, which include the Middle East, Africa, Asia, CIS, Europe, North America, and South and Central America, from 2023 to 2060 by taking the input data from 1980 to 2022 as shown in Figure 4 and Figure 5. Hydropower is a significant source with a 17% electricity share in Africa, which is expected to increase to 24% in 2040. In Africa, more than an 80% share has been recorded in some countries like Ethiopia, Zambia, Malawi, Uganda, Mozambique, and Democratic Republic of Congo. However, in Africa, the future of hydropower is forecasted to increase from 160.04 TWh from 2023 to 184.70 TWh by 2030, 216.76 TWh by 2040, 273.05 TWh by 2050, and 309.05 TWh by 2060. The Middle East has less hydropower development, particularly in countries with abundant water resources such as Iran, Turkey, and Egypt. Iran has the largest installed capacity of renewable hydropower in the Middle East at about 11 GW, followed by Egypt 21 GW, and the UAE 24 GW as of 2022. Climate change has also affected the reliability of hydropower generation in the region, with the net head and treated stream regime available being the most significant factors in determining the dependability of hydropower at a certain site. Therefore, past developments in the hydropower sector were very low in the Middle East; however, the future development is somehow forecasted to increase from 17.21 TWh hydropower production in 2023 to 24.03 TWh by 2030, 29.07 TWh by 2040, 30.78 TWh by 2050, and 32.86 by 2060.

Asia has 519 GW of installed capacity, and 1/3 will undergo modernization at an investment cost of USD 2.7 billion until 2030. Under this project, 21 hydropower plants with 45 individual stations of a 26 GW total capacity have been selected for modernization. Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh by 2030, 2772.06 TWh by 2040, 2811.41 TWh by 2050, and 3195.79 TWh by 2060. Hydropower in CIS countries, particularly in Central Asia, plays a significant role in the region’s energy landscape. The CIS includes seven countries, including Armenia, Azerbaijan, Georgia, the Kyrgyz Republic, Moldova, Tajikistan, and Uzbekistan, who have substantial hydropower potential. Tajikistan’s Nurek hydropower project generates about 50% of the country’s total annual energy demand, with an installed capacity of over 3 GW in the year 2022. Additionally, the Rogun Dam in Tajikistan, once fully operational by 2032, is expected to double the country’s installed electrical generation capacity to 3.6 GW. The CIS countries had a hydropower potential of 247.45 TWh in 2023, which will increase to 272.86 TWh by 2030, 295.95 TWh by 2040, 307.93 TWh by 2050, and 326.28 TWh by 2060. Hydropower in Europe is a significant contributor to the region’s renewable energy sector, accounting for 32% of the European Union’s renewable electricity generation and 12% of the total energy supply in the year 2022. The installed capacity of hydropower in Europe varies widely, with Norway having the highest capacity at 33.8 GW (96%) in 2022, followed by Switzerland, France, Italy, Germany, Austria, and Sweden. Austria is also among the countries with the highest overall number of existing hydropower plants, with many new ones still planned. Germany, despite meeting a lower share of its overall energy demand with hydropower, has most of its rivers in the South and center covered by hydropower plants. Europe has exploited hydropower potential, with a capacity of 610.93 TWh in 2023, which is expected to increase to 674.15 TWh by 2030, 714.70 TWh by 2040, 748.09 TWh by 2050, and 824.46 TWh by 2060.

Hydropower in North America is the largest renewable source, with 175 GW of installed capacity on the continent and contributing over 80% in the total energy mix. Presently, 75 hydropower plants are owned and operated by the United States Army Corps of Engineers (USACE), which produce 72 billion KWh every year. The forecasted potential of hydropower is high enough in North America for 2023 at around 730.08 TWh, which is then expected to reduce to 725.59 TWh by 2040 and then increase to 768.05 TWh by 2050 and 793.36 TWh by 2060. Hydropower is a significant source of electricity, contributing to 45% in South and Central America. The region’s unique geography and meteorology, with four large drainage systems, contribute to the widespread use of hydropower. The total forecasted hydropower potential in South and Central America will increase from 759.59 TWh in 2023 to 780.14 TWh by 2030, 833.11 TWh by 2040, 924.49 TWh by 2050, and 1055.48 TWh by 2060. Brazil, Chile, Argentina, and Colombia are among the countries with a substantial hydropower potential.

Global hydropower potential was also forecasted from 2023 to 2060 by taking input data from 1980 to 2022 as shown in Figure 6. Hydropower is a significant source of low-carbon electricity worldwide, accounting for about 16% of global electricity production in 2020, amongst all other green energy sources. The global hydropower potential is continuously increasing, from 4350.12 TWh in 2023 to 4806.26 TWh by 2030, 5393.80 TWh by 2040, 6003.53 TWh by 2050, and 6644.06 TWh by 2060. Hydropower can meet the fast-growing energy demand, and it produces very minute carbon emissions. However, hydropower construction has some negative impacts like population displacement, siltation and erosion patterns, loss of arable land, and disruption of natural ecology. Hydropower plants can also regulate the amount of water that passes through their turbines, which determines when electricity should be produced. This technique helps communities to fill up their energy gap anytime and restore plants during blackouts caused by ice storms, wildfires, hackers, or other disruptions to the electric grid.

Hydropower implementation around the globe has been faced with a challenging environment for implementation. The world’s hydropower capacity is expected to grow by 17% in this decade of 2021 and 2030. But this growth rate means only a 23% growth rate in net capacity addition over the previous decade due to the slow project development in key markets including China, Latin America, and Europe. China is still the largest market, contributing about 40% of the total capacity, in addition to the various networks; this share, though, has reduced by nearly 60% of the capacity from the previous period. At the same time, the level of activity is projected to rise in Asia Pacific, Africa, and the Middle East, with Chinese companies keenly financing and undertaking new ventures especially in sub-Saharan Africa and other emerging markets. To increase the potential for hydropower in meeting global energy and climate objectives, better policies and more effective financing structures are necessary. According to the International Energy Agency (IEA), to address the barriers of deployment, the addition of more hydropower capacity could be boosted by 40% in 2030. This involves such aspects as reducing approval lag by environmental issues or enhancing partnerships of risk sharing between the private and public sectors. Furthermore, rebuilding ageing hydropower plants is equally important to ensure reliable power supply, where such upgrade accounts for nearly 25% of total hydropower investment in developed economies. Thus, much more growth in hydropower plans is going to be required in order to meet the 2050 net-zero emission goals.

Organizations involved in the generation of electricity from hydropower have been experiencing several challenges that limit the expansion of this source of power generation worldwide. The first challenge is low investment levels noticed in the recent past, primarily due to low electricity prices in the global market. However, the current ongoing market structure is imbalance because other forms of renewables such as wind and solar power are highly subsidized to the extent of this disadvantaging hydropower. Consequently, the utilization of feasible hydropower resources is not complete in most countries, and some countries have developed only less than half of the feasible potential of hydropower capacity. Yet other important threats include weather patterns, which come in the form of unpredictable rainfall patterns and frequent droughts that are caused by climate change and affect the generation of hydropower. Such environmental changes influence water availability, the ability of catchments to provide higher yields of sediments, and natural hazards that could undermine continued hydropower development. In addition, most of the current hydropower facilities are old: about 40% of the existing facilities were established over 40 years ago, and, therefore, many of them suffer from obsolescence and degradation and will need upgrading and repair to continue serving their purpose optimally. Solving these problems requires practical approaches of creating relatively innovative solutions that would help meet global demands for energy while keeping more environmental needs in consideration and formulation of more comprehensive regulations on sustainability to ensure that the public and investors develop interest in new hydropower projects.

Figure 7 illustrates the validation of the LSTM applied to hydropower generation and water flow forecasting. The model’s accuracy was measured by the following metrics: MAE, RMSE, MAPE, and R². The smaller the MAE and RMSE values, the more accurate the model, and the lower the percentage of MAPE, the better the fitting of the forecasting; so, it is robust to outliers, and the lower the percentage, the better the reliability of the forecasting. The R² values around 0.99 indicate that predictions have a strong fit to observed data.

For validation purposes, we opted for a time-series walk-forward strategy because these types of data are sequential. In this model, as more training data was used, the window became larger and the model was checked by performing validations on the new, unseen time steps. With this approach, the sequence of events is not changed, and data is not disclosed like in the usual K-fold cross-validation.

To obtain reliable results, we computed each performance metric in separate windows and report the average findings in Figure 7. Looking at the standard deviations, it is observed that MAE (TWh), RMSE (TWh), MAPE (%), and R² (unitless) are 2.47, 1.78, 6.32, and 0.99, suggesting that there is consistency in each fold of the results whenever they are given.

The comprehensive study of the model shows that it is suitable and could be used in real-life settings. It may be useful to include bootstrapping-based techniques in the future so that estimations of uncertainty can be improved. Furthermore, the quantitative analysis of metrics such as MAE, RMSE, MAPE, and R² is presented in

Table 2. Result summary of proposed and existing models for hydropower forecasting.

Quantitative Evaluation of Models	LSTM (Proposed Study)	ARIMA [59]	GRU [60]	Prophet [61]	Classical Method (Random Forest) [62]
MAE (TWh), RMSE (TWh), MAPE (%), and R2 (Unitless)	MAE = 2.47	MAE = 2.77	MAE = 2.67	MAE = 2.89	MAE = 3.56
	RMSE = 1.78	RMSE = 2.11	RMSE = 1.27	RMSE = 2.13	RMSE = 2.98
	MAPE = 6.32	MAPE = 6.46	MAPE = 6.11	MAPE = 6.56	MAPE = 6.79
	R2 = 0.99	R2 = 0.78	R2 = 0.75	R2 = −0.83	R2 = 0.63

. The proposed LSTM model is compared with existing models like ARIMA, GRU, Prophet, and a classical method (Random Forest). It is seen that the LSTM model outperforms all other models, as it has better values in terms of hydropower forecasting.

Forecast reliability is a critical aspect in hydropower management, as accurate predictions of water inflow and energy generation are essential for optimizing operations and ensuring grid stability. Forecast reliability is highly dependent upon data quality, model training, and temporal dynamics, as these factors are all appropriately considered. Hence, forecast reliability is much greater. Further, biases in LSTM forecasts can stem from three sources, namely, historical bias, model assumptions, and hyperparameter tunning, as these factors are also appropriately considered and monitored in the model; hence, model bias is fine. Despite the advantages of the LSTM model, it sometime encounters failure due to data scarcity, concept drift, and complex interactions that may hinder its effectiveness in hydropower forecasting. However, these issues are all addressed in our proposed model; hence, the model has a higher accuracy. Furthermore, the computational cost and scalability of the LSTM model are highly dependent on training time, resource requirement, and scalability, and they considered during the deployment in hydropower forecasting. Hence, the model’s computational cost and scalability are appropriate. The overall comparative analysis of all regions in terms of past and future global energy is depicted in

Table 3. Comparative analysis of past and future energy forecast results in each region.

Region Year	Middle East Energy (TWh)	Africa Energy (TWh)	Asia Energy (TWh)	CIS Energy (TWh)	Europe Energy (TWh)	North America Energy (TWh)	South and Central America Energy (TWh)	Global Energy (TWh)
1980	9.68	48.31	273.44	183.93	464.76	550.06	201.44	1731.64
1990	15.27	57.25	399.27	211.23	502.56	612.44	360.80	2158.85
2000	10.72	75.25	521.65	208.43	617.75	662.59	550.31	2646.72
2010	17.39	107.68	1097.62	216.83	650.78	645.81	693.99	3430.13
2020	29.20	147.85	1864.63	263.23	660.95	696.26	696.86	4359.01
2030	16.77	211.85	2487.63	252.38	607.59	696.01	728.24	5412.20
2040	16.36	276.78	2742.78	253.01	601.24	698.55	739.88	6250.68
2050	16.33	313.17	2811.41	253.90	599.24	698.55	744.69	6666.84
2060	16.32	326.56	2828.05	254.52	598.50	698.68	746.82	6830.72

.

4. Energy Transition and Security Index Through Optimal Utilization of Hydropower

Energy is a multidimensional phrase that changes depending on the time, place, and goal. Heat waves and other extreme weather events are caused by the current global average surface temperature, which is now about 2 °C higher than pre-industrial levels [63]. Over 90% of the world’s population is forced to breathe dirty air, which has been connected to over 6 million early deaths annually. The energy sector is also the main contributor of this pollution [64]. In several nations, encouraging developments regarding increased access to clean cooking and energy have stalled or even reversed. To achieve the goals of net zero emissions by 2050, which is to keep global warming to 1.5 °C, significantly more work is still needed. However, energy transition and energy security indicators with proper implementation on global energy sector transformations would help to achieve long-term clean energy goals.

A comprehensive understanding of energy transition and energy security indicators is necessary in order to more accurately detect and evaluate vulnerabilities and threats related to energy systems. Energy transition and energy security indicators have been produced for the assessment of global energy systems by the World Energy Council (WEC), Asia Pacific Energy Research Center (APERC), Global Energy Institute (GEI), International Atomic Energy Agency (IAEA), European Commission (EC), and World Energy Forum (WEF) [65,66,67,68]. Energy transition and energy security indicators in the form of an index are used to measure a country’s economy. Further, these indicators can be helpful in a variety of situations such as identifying optimal energy solutions from a variety of possibilities, as well as noticing critical energy transition and energy security patterns that would otherwise go unnoticed, especially in developing nations. Energy transition and energy security indicators should be used to assess global energy systems and should be implemented, especially in developing and underdeveloped nations, for a proper evaluation of meeting energy demand through green energy, dissemination of heterogeneity of renewables, digital power system infrastructures, and sustainable energy solutions for managing long-run climate systems globally. Alongside some advantages, we explore some indicators for policy making related to energy transition and energy security that could affect future trends. A list of indicators is shown in

Table 4. List of energy transition and energy security indicators.

Dimension of Energy Transition	Dimension of Energy Security	Indicators
Security of clean energy supply	Availability	■ Renewable potential ■ Renewable share in total energy mix ■ Dependence on domestic renewable sources ■ Electricity per capita ■ Greater share of non-carbon ■ Easy access to electricity ■ Electricity consumption per household ■ Residential energy per capita ■ Security of energy supply and production
Internal energy market	Affordability	■ Cost stability ■ Energy export ■ Commercial intensity ■ Access ■ Industrial intensity ■ Equity ■ Agriculture intensity ■ Small percentage of income on energy ■ Gross domestic product per capita ■ Justice ■ Low energy poverty
Energy Efficiency	Technology and Efficiency	■ Energy intensity and efficiency ■ Energy reliability and safety ■ Resilience ■ Research on innovative technologies ■ Quality of supply ■ Stakeholder stability ■ Transmission and distribution losses ■ Meet energy demand sustainably ■ Non-carbon fuel ■ Self-sufficiency ■ Access to clean energy
Decarbonization	Environment and Sustainability	■ Climate change mitigation ■ CO2 production per energy consumption ■ CO2 production per household ■ CO2 production per capita ■ Climate change adoption ■ CO2 production per gross domestic product ■ SO2 production per capita ■ Minimizing destruction of forest ■ Water quality and availability ■ Minimizing air pollution in indoor activities
Innovation	Governance and Regulation	■ Regional connectivity and literacy ■ Promoting trade of energy fuels and technologies ■ Transparent energy models and lower corruption ■ Stability, legitimacy, integrity, and sufficient investment in energy sector ■ Decentralization and profitability

; these must be undertaken to shape energy transition and energy security policy at a global level of planning. Sustainable energy policy focused on accomplishing energy transition and energy security routes requires a balance of all indicators [65,66,67,68].

Also, to see if there is a discrepancy in energy transition and security analysis, Table 4 summarizes the findings. The set of variables listed in Table 4 have helped researchers identify gaps in energy transition and security studies.

Table 5. Gap analysis in energy transition and energy security studies based on indicators.

S. No.	Study	Considered Key Indicators	Considered Energy Transition Pathway	Considered Energy Security Pathway	Country/Region
1	[69]	No	Yes	Yes	European Union
2	[70]	No	Yes	Yes	Europe
3	[71]	No	Yes	Yes	South Africa
4	[72]	No	Yes	Yes	China
5	[73]	No	Yes	No	Russian–Ukraine
6	[74]	No	Yes	Yes	United State
7	[75]	No	Yes	Yes	Southeast Europe
8	[76]	No	Yes	Yes	Jordan
9	[77]	No	Yes	Yes	Ukraine
10	[78]	No	Yes	Yes	Nigeria
11	[79]	No	Yes	Yes	European Union
12	[80]	No	Yes	Yes	Norway, Finland, and Estonia
13	[81]	No	Yes	No	America
14	[82]	Yes	Yes	No	Russia
15	[83]	No	Yes	No	Australia
16	Our Proposed Study	Yes	Yes	Yes	Global

shows that some or all studies did not evaluate crucial indicators when formulating policies, that some studies solely considered energy transition paths, and that some studies concentrated on the country’s energy security. However, our study covers all essential indicators for global energy transition and security analysis policy in order to fill the gap.

Although several earlier studies have considered various scenarios and the performance of the hydropower sector, past research works have primarily focused on the energy sector or considered some or less renewable resources as a whole for sustainable energy transition and energy security. As depicted in

Table 6. Analysis for energy transition and energy security planning.

Study Reference	Study Purpose
[84]	Simulated the SHP plant and forecasted total capacity and load characteristics using the Monte Carlo method.
[85]	Policy formulated in South Korea’s transition to renewable energy.
[86]	Simulated a hydropower system for capacity design and the parallel operation of hydro turbines and forecasted the net present value and internal rate of return using a proprietary multi-objective evaluation algorithm.
[87]	American transition to fully green energy systems was evaluated.
[88]	Simulated the operating mechanism of an SHP plant and forecasted operational costs and energy generation using a nonlinear constrained technique.
[89]	Germany exploited indigenous sources for developing sustainable energy systems.
[90]	Developed a hydropower station and designed turbines, a penstock, a generator, and a draft tube and forecasted the payback period and return on investment.
[91]	Green energy policy is suggested to implement in Indonesian energy sector.
[92]	Technical and economical investigations were conducted for local and global markets’ financial situations, and sensitivity analysis was performed.
[93]	Tunisia’s renewable energy transition policy is developed through an autoregressive model.
[94]	An optimal location for hydropower was identified, and the hydrological parameters were simulated.
[95]	An Indian case study that demonstrates the use of a model-based method to plan the transition to renewable energy in an electricity system with limited resources.
[96]	Sensitivity analysis was performed for low-head dam and canal-based SHP schemes, and the capacity of hydropower stations was forecasted.
[97]	An analysis of Jordan’s transition to 100% renewable energy by 2050.
[98]	A regression analysis technique was implemented on a high-head run-off river plant for head and runner diameters.
[99]	A stochastic cost econometric frontier method was employed for forecasting the labor cost, capital investment, and water used in the energy generation process.
[100]	Simulating the carbon footprint of the switch to renewable energy in Australia’s electrical sector.
[101]	Mado–Watt simulation tool (MATLAB) was used for repowering old hydropower stations and forecasting hydraulic losses.
[102]	Reshaping old power systems to renewable energy transition in the Caribbeans.
[103]	The regression analysis method was adopted for forecasting the energy and cost of electromechanical equipment.
[104]	Energy transition implementation through storage devices in Mexico.
[105]	Investment analysis was performed for small and medium hydropower stations.
[106]	Plant capacity and net head of hydropower station were designed to identify the cost of electromechanical equipment.
[107]	A clean climate strategy was developed for the United Kingdom.
[84]	The renovation cost of a hydropower station was forecasted through economic indices (discounted payback period and net present value).
[108]	An energy transition strategy was developed for Europe to evaluate possible ways to exploit green energy.
[109]	Cost benefit ratios and annual rate of return for mini and pico hydel schemes were identified.
[110]	A system dynamics approach was developed to evaluate the energy sector of Morocco.
[111]	Regression and correlation analyses were performed on canal-based and run-off-river-based SHP plants.
[112]	Refurbishment and uprating analyses were performed for order number of discharges, head, and interest rate evaluation.
[113]	Japan’s energy sector was placed on transformations for environmental and economic concerns.
[114]	Forecasted per-unit energy cost of hydropower station.
[115]	Clean energy targets were developed in Russia through an optimal utilization of solar energy.
Proposed Study	Our proposed study resolves the global issue of climate change and limits the global mean temperature to no more than 1.5 °C by the exploitation of untapped hydropower globally, which further accelerates energy transition and stabilizes global energy security. In our proposed study, the LSTM model was developed for global and regional hydropower forecasting for the study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have a greater hydropower potential as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent States (CIS), Europe, North America, and South and Central America. The global hydropower potential is sufficient for achieving energy transition and energy security goals. The accuracy and performance of the LSTM model was found to be 98%. The application of this study would limit the increasingly more frequent and severe heat waves, droughts, intense rain, and floods around the globe.

, previous studies have not included such a thorough examination of the hydropower sector that covers all critical factors. This study is a key source of information about the global hydropower sector. Furthermore, the data for past studies was gathered from peer-reviewed papers and reports published on a global scale, as well as pertinent data from other web resources. So, this assessment highlights the most important characteristics of global hydropower resources and lays the groundwork for future research. This work will improve the understanding of the hydropower sector and facilitate policy and decision-making in the energy–environment–economic–social nexus.

5. SWOT Analysis Framework for Global Hydropower Exploitation

SWOT analysis is widely used in academia and industry to assess a project’s strengths and weaknesses as a strategic planning tool. It also emphasizes the benefits of possibilities and warns of potential hazards that could cause a delay in achieving stated objectives [116]. The framework of the SWOT analysis for the global hydropower sector is presented in

Table 7. SWOT analysis for global hydropower development.

Strength	Weaknesses
■ Zero fuel transportation cost ■ Availability of clean technologies for hydropower development ■ Abundant potential of hydropower exists ■ Reliable and secure energy source ■ Clean and green source of energy	■ Large seasonal variation ■ Chances of reservoir degradation ■ Development and commissioning of hydropower plants require greater time ■ Requires huge amount of capital
Opportunities	Threats
■ Capacity of dams increases with small investments ■ Mitigate flood risk ■ Water storage dams provide benefits for irrigation and fisheries departments ■ Abundant power can be produced from SHP sites ■ 93% of hydro potential is still unexploited ■ Sustainable energy supply at a low cost and reduced energy demand–supply gap in the country ■ Social and economic benefits	■ Dominance of thermal energy ■ Financial issues ■ Corruption and nepotism ■ Political instability ■ Technical issues ■ No focus on the development of small hydro plants ■ Environmental aspects ■ Water resource transboundary location ■ Social issues ■ Inconsistent energy policies ■ Behavioral aspects ■ Absence of environmental externalities ■ Improper function of governmental organizations

. In the light of a SWOT analysis, policymakers can have an insight into the strengths of current policies for hydropower development and the issues that remain uncovered alongside opportunities and threats. A SWOT analysis analyzes strengths and weaknesses inherent in an organization, as well as opportunities and threats posed by the external environment. It creates a platform where stakeholders may act on strengths and opportunities regarding hydropower development while, at the same time, working on the weaknesses and threats of the same development. Hydropower performance can then be used by investors when determining the feasibility of projects as indicated by the SWOT analysis. Knowledge of what economic feasibility implies, including the positive factors including employment creation and energy cost savings, also helps to understand challenges like high initial costs and negative effects on the environment in coming up with good investment decisions. It may also be useful where there are potential beneficiaries of the report who want to identify negative effects of the hydropower projects on the environment like ecological effects of the construction of dams. When these are identified, stakeholders can put a plan in place to prevent the adverse impacts and also bring about sustainability. Due to climate change impacts such as reduced water availability and changes in water flow patterns, a SWOT analysis is capable of identifying the threats of hydropower generation. It is only when we unravel these dynamics that we can be in a better position to plan on how to mitigate against climate variability. Opportunities that may exist in technological development include the potential to improve the design of the hydropower industry, such as through better turbines or energy storage, which can further strengthen the use of hydropower as a renewable energy source.

6. Policy Implication

Policy makers should consider the following implications for the global development of hydropower generation capacity:

• Establish policies requiring governments and energy agencies to collect and standardize at least 8 to 10 years of monthly hydroelectric generation data to train robust LSTM models.
• Invest in centralized repositories for hydro-meteorological data to improve model generalizability across regions.
• Develop international agreements for sharing transboundary water resource data, such as river flow and reservoir levels, to enhance LSTM model performance in multi-country hydropower systems.
• Prioritize regions with high hydropower potential in national energy transition roadmaps by implementing LSTM insights for capacity expansion.
• Address ethical risks, such as over-reliance on automated predictions, by mandating human oversight in critical decision-making.
• Train utility operators and policymakers in interpreting LSTM outputs to align technical forecasts with strategic investments.
• Support research into hybrid models combining LSTM with climate projections to improve resilience against droughts and extreme weather.
• Offer tax breaks or grants for companies developing LSTM tools tailored to small-scale or decentralized hydropower systems, enhancing energy access in rural areas.
• Foster public–private partnerships to deploy forecasting models in regions lacking historical data, using transfer learning techniques.

7. Conclusions

The severity of climate change’s effects varies across regions, with some areas experiencing severe drought while others face increased flood risks. Climate change also exacerbates existing health inequalities, disproportionately affecting low-income and disadvantaged countries and communities. To address climate crises, urgent action is developed in this study to limit temperature rise to 1.5 °C. In the study, regional and global hydropower potentials are forecasted using an LSTM-based model for the study period of 2023 to 2060 by taking input data from 1980 to 2022. Further, an evaluation of the LSTM model was performed using the MAE, RMSE, MAPE, and R². Amongst all regions of the world including the Middle East, Africa, Asia, CIS, Europe, North America, and South and Central America, Asian countries have a greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh by 2030, 2772.06 TWh by 2040, 2811.41 TWh by 2050, and 3195.79 TWh by 2060. The global hydropower potential was forecasted, which shows a continuous increase from 4350.12 TWh in 2023 to 4806.26 TWh by 2030, 5393.80 TWh by 2040, 6003.53 TWh by 2050, and 6644.06 TWh by 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM model identified though MAE, RMSE, MAPE, and R² were found to be 98%.