## 1 Introduction
This rich dataset enables the development of predictive models and provides insights into the health conditions and remaining lifespan of lead-acid batteries. A promising approach for prediction is Gaussian process regression (GPR). This is particularly useful in solving regression problems. GPR models the relationship between the input and output variables by assuming a Gaussian distribution over functions. It provides a flexible and non-parametric approach to regression, allowing accurate predictions with uncertainty estimation. One advantage of GPR is its ability to handle non-linear relationships and capture complex patterns in the data. It can be used with various types of data structures using different kernel functions [16]. The predictive distribution of the GPR for a new input location can be derived in a closed form, allowing for efficient prediction [17]. However, predicting new inputs using GPR can be computationally inefficient when dealing with large datasets [18]. To address this issue, researchers have proposed various approaches to accelerate GPR. One approach is the use of quantum algorithms, which can achieve quadratic speedup over their classical counterparts [18]. Additionally, the use of localization kernels has been explored to down weigh the contributions from distant data points, resulting in considerable speedups compared with the standard GPR [19]. GPR is a powerful machine-learning technique that can capture complex patterns and correlations in data, enabling accurate predictions and uncertainty estimation [20; 21; 22; 23]. Support Vector Regression (SVR) and Random Forest (RF) are machine-learning algorithms commonly used for non-linear prediction tasks. SVR is a regression algorithm that uses support vector machines to perform a non-linear regression. It aims to find a hyperplane that best fits the training data while minimizing error. SVR can handle non-linear relationships between the input variables and the target variable by using kernel functions to transform the data into a higher-dimensional space where a linear relationship can be found. The choice of kernel function can significantly affect the performance of SVR models [24]. The RF is an ensemble learning method that combines multiple decision trees to make predictions. Each decision tree is trained on a random subset of the training data and a random subset of the input features. RF can handle nonlinear relationships by combining the predictions of multiple decision trees, which collectively capture the complex interactions between the input variables [25]. Both SVR and RF have been shown to be effective in non-linear prediction tasks. For example, in the context of short-term load forecasting, a hybrid model that combines SVR, gray catastrophe, and RF has been developed and shown to outperform alternative models in terms of forecasting accuracy [25]. In this case study, P.T. XYZ has not yet utilized the potential of the Internet of Things (IoT) technology for monitoring and Machine Learning to predict future voltage and degradation values. PT XYZ is an oil and gas (O&G) company that operates in the Rokan Block working area and contributes to the fulfillment of national energy needs. P.T. XYZ has 14 generator sets (gensets) placed at several vital operational locations such as data centers, towers, and gathering stations. Generators function as power backup sources for IT devices such as routers, switches, and trunked radio systems, to maintain connectivity and communication when the main power source is disrupted or fails. Currently, battery monitoring is performed manually. It requires visits to the generator location once a week to inspect the battery voltage. The implementation of an automatic monitoring system is necessary because of the critical functions of these generators. If a generator fails, it can disrupt oil operations and affect the achievement of the daily lifting targets. Periodic manual monitoring may not be able to detect changes or issues that occur between the monitoring intervals.