Improving Predictions of Renewable Energy Systems with the Application of Machine Learning Integrated with Explainable AI

The widespread adoption of renewable energy technologies such as wind power, solar, ocean power, geothermal power, hydroelectric power, hydrogen power, and bio-power has received international attention because of their beneficial environmental effects, high rate of technological development, and consistency with long-term targets of reducing climate effects. Some of these achievements notwithstanding, the actual generation and consumption of renewable energy is very difficult to predict, which poses a problem. The non-stationary and discontinuous characteristics of the renewable sources and the intricate interdependence on weather, economy, and infrastructure render classical forecasting effects, not sufficient in modeling the nonlinearities and the dynamism of contemporary energy networks.

Causes of these complexities have been solved by machine learning (ML) and deep learning (DL) models as they can learn patterns to be used in large, high-dimensional datasets. But there are two major shortcomings that still exist: a large number of ML models are not interpretable, effectively a black box, and the models cannot be used to detect nonlinear relationships or changes over a time period. It is these shortcomings that force the main scientific question of this study to be: how can current models of renewable energy that make predictions be improved so that they not only improve on the accuracy of their predictions but also become more explainable under the influence of the complications of the market systems and nonlinear trends in the data? To overcome this, the current study considers a hybrid system that resorts to high-performance ensemble ML models and explainable artificial intelligence (XAI) methods. An XAI system plays a crucial role in the establishment of processes, methods, and approaches that elicit comprehensible explanations of the latest ML models [1].

XAI is fundamental to disentangling AI decision-making, especially in the energy sector, where the implications of automated decision automation become of paramount importance to regulatory approval and functional trust. With renewable energy, XAI makes it easier to comprehend or explain the ML predictions in a renewable energy system and it allows stakeholders to become aware of how and why the system works; therefore, more effective energy solutions are promoted, as well as less costly and even more reliable energy solutions.

The energy sector encounters several issues regarding the methods of production, transmission, and distribution, namely, the management of costs, security of the system, efficiency of operation, and inconsistency of carbon footprint. What makes this happen is the dynamic amount of data that is put through energy companies, which require data to be processed, stored, and analyzed in a quest to optimize services and minimize risk. To overcome these issues, AI technologies are used more and more and have a positive impact by reducing energy consumption, stabilizing demand, increasing the reliability of the grid, and detecting issues, including natural gas leaks [2]. Also, Khalil and Enjadat [3]. focused on a better understanding of the regional climate adaptation approaches, vide its case location in Karak City only, whereas support local data analysis is necessary to address global climate variability.

The emergence of AI has also been augmented with accelerated developments within the ML and DL methods. The integration of AI in the energy infrastructure is also represented by smart grid technologies that are supposed to power a 15 trillion market ecosystem by 2030 [4]. And all by itself the renewable energy field is expected to grow to 75.82 billion by 2030 with an increase in demand of clean energy and intelligent systems. These patterns explain why it is necessary to digitalize energy systems, and AI has a game-changing role in the processes [5]. In that regard, XAI approaches, including one known as Local Interpretable Model-Agnostic Explanations (LIME), gain importance not only over the model transparency, but also as tools to make the insights generated by AI, be understandable and actionable by the human decision-makers. Incorporating XAI into the foundation of the ML prediction pipeline, this paper proposes to move further on the level of the forecast accuracy and model transparency, providing a capable framework that would not only improve the results of the modern black-box solutions but also provide a basis to interpretable models. This twofold contribution of performance and explainability directly addresses the changing demand of energy analysts, operators, and policymakers who are forced to work in a more data- and more diligently complex energy environment.

1.1 Motivation

The escalating demand for energy in recent years has been propelled by factors such as industrial growth, economic expansion, and population increases across the globe. As a result, critical challenges such as ensuring energy security, supporting economic development, and minimizing greenhouse gas emissions have intensified. The environmental impact of fossil fuel dependence has made the transition to sustainable energy sources imperative. Renewable energy is increasingly recognized as a crucial, eco-friendly alternative to conventional energy sources. However, adding renewable energy to current grids raises unique challenges. The sensing of these renewable sources, such as wind and solar, is inherently intermittent, leading to variable outputs of energy. As such, accurate forecasts of not only energy demand but also the production of renewable energy are essential in keeping supply and demand in balance, optimizing production, and planning the future of the infrastructure.

ML models come up as an effective solution to tackle these forecasting problems. They are better at identifying complex, non-linear relationships in historical data than traditional forecasting methods. Furthermore, the use of XAI makes the predictions more interpretable. The trustworthiness of the predictions for stakeholders and policymakers is enhanced by XAI, which makes the results clear and explainable. In order to provide insight to enable the worldwide adoption of renewable energy further, this study hopes to develop both accurate and interpretable models to utilize the energy consumption of renewable energy over time.

The proposed methodology outlines, shown in Figure 1, a comprehensive approach to analyzing sustainable energy data with the aim of improving energy consumption forecasting. First, the company collects data from a global sustainable energy database. We perform extensive exploratory data analysis (EDA) on this dataset to characterize it better, check for missing values, and check the distribution of some important variables, such as CO₂ emissions. In this stage, statistical summaries and visualizations are used to identify the patterns and outliers in the data.

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Figure 1. Flowchart showing the proposed methodology for data processing and analysis

The data undergoes different preparation steps after analysis to handle missing values and normalize data types. A significant part of the methodology constructs an essential binary target variable that categorizes points into two groups based on meeting a particular threshold for renewable energy use. Central to this methodology is the application of several ML models to predict high renewable energy usage. These could include decision trees, ensemble methods, advanced gradient boosting, and DL models. The methods are compared based on their predictive performance to find the best solution. Then, all performance metrics, such as accuracy, recall, precision, and F1 score, will be calculated to have a clear view of the performance of each model.

Transparent and interpretable ML models are possible because of XAI techniques, which the methodology integrates. Understanding and trustworthiness of model predictions are guaranteed by this approach, which benefits both the stakeholders and energy policymakers. This proposed methodology is, therefore, a combination of detailed data analysis, advanced ML, and explainable AI that improves the prediction of renewable energy consumption. Developing robust and interpretable models capable of facilitating the adoption of sustainable energy practices on a global scale requires a holistic approach at this level.

3.1 Dataset overview

The publicly available dataset used in this study is called Global Data on Sustainable Energy, and is prepared by Tanwar [35] and published on Kaggle. The dataset shows a multeity, multi-country panel of sustainable energy indicators that extends across 2000-2020. It constitutes a wide range of variables necessary in the study of global energy transitions, including access to electricity, utilization of clean cooking fuels, installed renewable electricity generation capacity per capita, and fuel-source breakdown of electricity generation in fossil fuels, nuclear energy, and renewables. Others include CO₂ emissions per capita, the share of low-carbon electricity, the degree of energy intensity, primary energy consumption per head, and other important economic indicators such as the growth of gross domestic product and gross domestic product per capita. Geographic metrics, like the population density, the territory of a country, and geographical coordinates, are also implemented in the dataset, together with the indicators of the international financial flows focusing on the clean energy programs in the developing world. These features of the dataset render it especially useful to evaluate the progress made towards Sustainable Development Goal 7 (SDG 7), to carry out cross-national comparisons and carry out temporal trend analysis, as well as to build ML models that predict energy consumption and carbon emissions. Its design and breadth enable the transparency of sound statistical modeling and policy analysis, particularly last-mile explainable AI approaches to better interpretability in renewable energy modeling.

Table 2. Description of features in the sustainable energy dataset

There is one entry layer per country, in this case, called an "Entity", and annual data points for the years 2000 to 2020. It tracks the share of the population with access to electricity, the percentage dependent on clean cooking fuels, and installed renewable energy capacity per capita, and it does so meticulously. The dataset also measures the financial capital coming into developing countries for clean energy projects in U.S. dollars. In terms of energy production, the dataset tracks electricity generated from fossil fuels, nuclear power, and renewable sources, expressed in terawatt-hours. It further details the share of renewable energy in the final energy consumption and the percentage of electricity derived from low-carbon sources, which includes both nuclear and renewable energy. Additionally, it measures primary energy consumption per capita, energy usage efficiency relative to GDP in purchasing power parity terms, and CO₂ emissions per capita. Other recorded metrics include the equivalent primary energy derived from renewable sources, annual GDP growth rate, GDP per capita, population density, total land area, and the geographic coordinates (latitude and longitude) of each country's centroid, as shown in Table 2. This structured overview of the dataset encapsulates the breadth and depth of the data available for analysis, facilitating a comprehensive understanding of global sustainable energy trends and supporting the evaluation of progress towards achieving broader environmental and developmental goals.

3.2 EDA

The EDA conducted on the dataset provides a fundamental understanding of the key metrics related to sustainable energy across various countries from 2000 to 2020. Initially, summary statistics offer a glimpse into the distribution of each feature within the dataset, encompassing measures of central tendency and dispersion. The analysis identifies any missing values across different columns, which is crucial for maintaining the integrity of subsequent analyses. Missing values were managed through strategies that include the omission of incomplete records and imputation with median values, where pertinent. The metric underlying dataset attributes is represented mathematically, for example, mean, standard deviation, expressed as:

$\mu =\frac{1}{n}\underset{i=1}{\overset{n}{\mathop \sum }}\,{{x}_{i}}~\text{and }\!\!~\!\!\text{ }\sigma =\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathop \sum }}\,{{({{x}_{i}}-\mu )}^{2}}}$    (1)

where, $\mu $ and $\sigma $ are the mean and standard deviation, respectively, and ${{x}_{i}}$ are the data points.

The correctness and relevance of the dataset columns are then verified to ensure that further analyses are performed on correct and comprehensive data. The year column is shown in bold during formatting checks, with the requirement that it is an integer, which is particularly important for time-series analysis. Figure 2 presents the first ten energy-related measures with the greatest number of missing values. The variable with the highest number of missing records is 'Renewables (% equivalent primary energy)' with 2,137 missing values, followed by 'Financial flows to developing countries' and 'Renewable electricity per capita'. Such incompleteness highlights the challenges of data unavailability in sustainability and energy indicators. This skew indicates that while a few countries have high emissions, most maintain relatively low CO₂ outputs.

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Figure 2. Metrics with most missing values

Figure 2 demonstrates the first ten energy-related measures that have the greatest amount of missing values in the database. The variable that has the highest number of missing records is the variable whose title is Renewables (% equivalent primary energy) with 2,137 missing values, followed by the variable financial flows to developing countries, as well as that of Renewable electricity per capita. Such high incompleteness emphasizes high levels of data unavailability with regard to important indicators connected with sustainability and energy changes. The solution to such empty values is important to the robustness and validity of the analysis and the policy recommendations that lie down the road. Continuing the EDA, Figure 3 shows the distribution of CO₂ emissions across countries. The chart highlights disparities between nations, with a small number of countries producing very high emissions while the majority maintain lower levels. This provides insight into global emission inequalities and the concentration of environmental impact.

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Figure 3. Distribution of CO₂ emissions by country

In further examination of the EDA, Figure 4 illustrates the trend of the CO₂ emissions by the leading five global smokers. The graphic assists in breaking down highs and lows in the emissions which illuminate the success or failure of the environmental policies and the impact that economic growth has on the green initiatives. Although the given dataset has a vast scope of renewable energy indicators of several countries during the period between 2000 and 2020, it is necessary to evaluate its completeness and representativeness. The data set encompasses extensive socioeconomic, environmental and energy-specific characteristics of almost all the countries that are well-known worldwide, thus providing a macro perspective on sustainable energy trends. There may be partial records or nil values of some countries because of unstable reporting by countries or limitations on collecting data. To reduce this a number of imputation methods were employed, including median substitution or record deletion, depending on the situation. Nonetheless, the volume of data is sufficiently large to allow trend analysis at a global scale, comparative screening, as well as ML analysis due to the presence of such gaps. Its infrastructure reflects regional differences and long-term trends so that it is indicative of renewable energy development worldwide, although it is important to report the results with caution on a narrower scale, including national or sub-regional areas where the lack of data could still be the reality.

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Figure 4. CO₂ emissions over time by top 5 countries

3.3 Preprocessing

The preprocessing stage of the analysis pipeline is crucial for preparing the dataset for subsequent machine-learning tasks. Initially, the dataset is loaded into a panda Data Frame from a specified path. As such, this dataset includes multiple data types with clearly different preprocessing strategies. Numerical columns had any missing values filled with columns meant to maintain the numerical stability of calculations while keeping the distribution of columns intact. Mathematically, this imputation can be expressed as:

${{\mu }_{j}}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathop \sum }}\,{{x}_{ij}}$   (2)

where, ${{\mu }_{j}}$ is the mean of the ${{j}^{th}}$ column, and ${{x}_{ij}}$ indicates the ${{i}^{th}}$ data in column j.

For categorical data, missing values are filled with the mode per column, which keeps the most relevant category in each feature. Based on the 'Renewable energy share in the total final energy consumption (%)', a binary target variable is then generated based on whether the value is above a defined threshold, in this case, 20%, turning this into a classification problem. The next step after cleaning the data, selecting the features, and finding out which variables is most useful in predicting the target. Other selected features include access to electricity, the use of clean fuels for cooking, various metrics of energy production, etc. We then split these features into training and testing subsets, making sure the distribution of data is representative of the average case in both subsets. The last step is to normalize the range of its features using a standard scaler. Many ML algorithms work better when input data is normalised, especially algorithms that consider the size of their input data. The scaling process is defined as:

$x{{'}_{ij}}=\frac{{{x}_{ij}}-{{\mu }_{j}}}{{{\sigma }_{j}}}$    (3)

where, $x{{\text{ }\!\!'\!\!\text{ }}_{ij}}$ is the scaled value, ${{\mu }_{j}}$ and ${{\sigma }_{j}}$ are the mean and standard deviation of the ${{j}^{th}}$ feature in the training data.

Although tree-based models like Decision Tree, RF, or LightGBM do not objectively need normalization, we used Minmax scaling on all features to allow a uniform comparison between all the features and the models since we want all of them to be represented in the same way to make fair comparison. Such normalization is specifically useful to those algorithms that optimize with distance or gradient-based optimization, which include Gradient Boosting and XGBoost, and results in better convergence and consistent results.

A bar chart as shown in Figure 5 displaying the distribution of classes is generated to visually check the balance of the classes in the target variable. This visualization helps in capturing the skewness or balance in the target variable, which guides the possible strategies to be taken during model training or how the data sampling technique can be adopted in the process.

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Figure 5. Distribution of classes in the "high renewable energy share" label

3.4 ML models

The models were chosen depending on their suitability theory of renewable energy data. Modeling mixed data types and nonlinearity can easily be modeled with trees, whereas strong results can be achieved with ensemble or boosting algorithms such as XGBoost or LightGBM on structured data and regularization, and large datasets in the real world, such as large energy-related data sets.

This analysis utilizes a suite of ML models with carefully tuned parameters specific to each model to optimize the predictive accuracy for classifying the share of renewable energy consumption. Selecting models and tuning model hyperparameters is also crucial, as the choice made at this stage governs the performance of the models and the applicability of the findings within real-world systems.

The selection of the ML models employed in this research work was empirical as well as theoretical in terms of being compatible with the characteristics of renewable energy data, such as nonlinearity, intermittency, and multivariate effects, among many others. Tree-based models Tree-based models (e.g., Decision Trees, RF, Extra Trees) will treat heterogeneous features and noise well. The models that are most applicable to the modeling of the highly complex and sparse model, in particular, the imbalanced dataset, such as in solar and wind energy adoption, include boosting (e.g., Gradient Boosting, XGBoost, LightGBM).

LightGBM, specifically, is particularly effective in the case of big and skewed data sets because of its efficient leaf-wise tree building. The resulting alignment will make sure that the selected models are fit to the structural complexity and variability of renewable energy systems.

• Decision Tree Classifier: The Decision Tree model has a maximum depth of three layers. The depth was chosen to avoid having the model become too complex and overfit the training data. Reducing the complexity of a tree helps with the generalization of the unseen data.
• RF classifier: This ensemble model, based on 30 trees (n_estimators=30) has a maximum depth of three and a maximum number of features used for splits equal to two (max_features=2). By considering multiple trees, we are reducing variance and not overfitting ourselves in the dataset, which allows for robust performance in the presence of noise.
• Extra Trees classifier: Similar to RF, the Extra Trees classifier is defined with 30 trees and a max depth of five. It uses a different method for splitting nodes, using a randomly selected threshold per feature rather than the optimal threshold as seen in RF, which leads to a quick training process at the cost of some increase in variance.
• Gradient Boosting Classifier: Gradient Boosting is configured with 10 boosting stages (n_estimators=10) and a steep learning rate (1.0) with maximum tree depth 2. This aggressive learning framework is designed to converge upon a low-error model speedily, but with the downside of a low error ceiling that is increasingly approaching zero as training proceeds, requiring careful management to avoid overshooting the minimum error.
• AdaBoost Classifier: AdaBoost uses 50 weak learners (n_estimators=50) in sequence, focusing on correcting the mistakes of the previous learners in the chain. This method is effective in reducing bias and variance, especially in complex classification tasks.
• XGBoost: The XGBoost model is extensively tuned with a maximum depth of three, learn- ing rate of 0.102, and 484 trees (n_estimators=484). It also adjusts the model complexity (gamma=0.93) and minimum child weight (min_child_weight=9.91) to control overfitting.
• LightGBM: The LightGBM model uses 100 trees with a learning rate of 0.1 and a depth of five. Known for its efficiency on large datasets, LightGBM builds tree leaf-wise rather than level-wise, resulting in faster learning and better accuracy on imbalanced data sets.

Each model's parameters were selected after a series of validation tests to balance the bias-variance trade-off effectively. The configurations are designed to maximize the predictive performance while ensuring that the models remain computationally feasible and interpretable.

Manual tuning of the hyperparameters based on the exploratory experiments on the training set was carried out to select the best hyperparameters of each model. In case of simpler models as Decision Tree and RF, we tuned parameters, i.e., max_depth, n_estimators, and even max_features by examining performance values (accuracy, F1 score, etc.) on the validation split based on the training data and performed this process with iterations, as shown in Table 3. In more complex models like XGBoost and LightGBM, the randomized search was used on a pre-set range of hyperparameters values, which in case of XGBoost is learning rate, max_depth, n_estimators, gamma and min_child_weight, and much the same these values in the case of LightGBM. The objective of such a process was to find a trade-off between the complexity of the model and performance of generalization by providing robust, observable outcomes with computationally controlled costs.

Table 3. Hyperparameter tuning methods and search ranges for each ML model

To ensure robust evaluation, the data is split into training and testing sets in an 80-20 ratio, and features are scaled using a standard scaler to normalize the data distribution, which is crucial for models that rely on distance calculations like k-nearest neighbors.

3.5 Explainable AI-LIME

In the realm of ML, particularly in contexts requiring high-stakes decision-making, the interpretability of model predictions is paramount. LIME is a method also developed in response to the lack of transparency of  'black-box' models: its function is to help interpret decisions made by such models, for example, DNN and complex ensembles. The core idea behind LIME is to approximate the local prediction behavior of these models in the neighborhood of a particular prediction. LIME creates a dataset located in the neighborhood around the instance to explain by perturbing it. These samples are then used to train an interpretable model (usually a linear one), which is less complex and more transparent than the original. The fundamental idea is to weigh the noisy samples by how close they are to the instance of interest so that the local surrogate model can be a good approximation at that point. The weighting function is mathematically represented as:

$\omega(x)=\exp \left(-\frac{\left\|x-x_0\right\|^2}{2 \sigma^2}\right)$    (4)

where, $x$ represents a perturbed sample, ${{x}_{0}}$ is the original instance, and $\sigma $ is a bandwidth parameter that controls the scope of locality. The weights $\omega \left( x \right)$ decay exponentially far from ${{x}_{0}}$, concentrating the learning of the surrogate model on the locality around the instance.

An interpretable surrogate model trained on this data can then be used to represent the model behavior around ${{x}_{0}}$, that is, which features contribute to/drive the model output. For example, in a classification problem, LIME might show that certain features made a positive or negative contribution to the class prediction, helping provide actionable insight as to why the model made that particular prediction. LIME model success depends on how suitable the features are for use in the surrogate model structure. Models select features regarding their influence on prediction outcomes and their registered importance according to model metrics. Each feature gain or loss of influence has a direct impact on the linear surrogate model expressed through its coefficients. This transparency helps create more trust in those models and allows them to be deployed in sensitive and higher stake environments. In addition, these kinds of explanations are extremely useful for debugging models, ensuring regulatory compliance, and enabling a cycle of iterative improvements to the algorithms.

These findings indicate the performance of ML models in forecasting the use of renewable energy and the enhanced performance of predictive analyses and explainability in forecasts due to the implementation of XAI strategies. LightGBM and other models like XGBoost performed well on multivariate energy data, which shows their capabilities to apply in real life energy prediction tasks. The models were guided by the use of the LIME framework, which offered insightful information of factors that were critical predictors, thus generating more trust and enabling wider adoption of AI-based solutions to the energy industry. Such an XAI integration provided a clear and viable method of feature interpretation that can aid analysts and policymakers in propagating data-driven renewable energy policies. The approaches suggested in the current research add to a powerful AI-based forecasting system, boosting the level of transparency and decision-making ability.

A number of technical issues need to be addressed in renewable energy forecasting conditions in real-time: this is a constant flow of streams with live data, consistent updates of the prediction model, and computing limitations. In this dynamic state, securing the stability and reliability of the model involves effective data pipelines, as well as missing or delayed input robustness and periodic retraining with the concept of adapting to the shift in the meaning of the concept. Whereas the current study will use historical information, future research will be undertaken in the formulation of a scalable deployment pattern that can support the formation of the real-time environment with minimal delays and decline in the performance. It would also be useful to add on the existing models by incorporating more data that are detailed and giving explanations that not only have transparency but also action and interpretation. Finally, the optimized models will go far in terms of influencing the real-time renewable energy forecasting, as they will be reliable, explainable and operational to the energy providers and the consumer.