## 2 Related Works  
The main purpose of this work is to select an optimal time-series forecasting model that meets user requirements by balancing model performance and execution time, while considering available computing resources. To establish an effective model selection framework for this purpose, it is crucial to understand the characteristics and limitations of existing approaches in time series forecasting and energy management systems.  
Previous research has explored diverse strategies for model selection, tracing its evolution from early rule-based systems to modern automated and meta-learning-based approaches. [PERSON] et al. [14] introduced a foundational rule-based approach that employed 99 predefined rules and 18 time-series features, focusing on trends, uncertainty, and characteristic patterns to guide model choice. Building upon these early methodologies, [PERSON] et al. [15] proposed a meta-learning approach for time-series forecasting, leveraging features such as length, skewness, and kurtosis to learn their relationship with model performance. Subsequently, automated platforms such as AutoGluon [16] and AutoKeras [17] were developed to streamline model selection through automated hyperparameter optimization and architecture search, reducing human intervention. More recent studies have further advanced meta-learning-based methodologies: [PERSON] et al. [18] applied meta-learning to COVID-19 forecasting using ARIMA and TBATS models; [PERSON] et al. [19] integrated considerations of explainability, model complexity, performance, and carbon emissions; [PERSON] et al. [20] enhanced selection accuracy through clustering and weighted representation learning; [PERSON] et al. [21] introduced a decision-support system using stacking-based meta-learning for LPG consumption forecasting without distinguishing user types. Despite these advancements, most existing approaches primarily focus on optimizing prediction accuracy based on static data features while neglecting the critical trade-offs between model performance, execution time, and dynamic resource availability. This gap limits their applicability in real-time, resource-constrained environments such as edge-based energy management systems. Table 1 summarizes these existing model selection approaches side by side, highlighting each method's strategy and its lack of resource awareness.  
To overcome these limitations, this gap motivates our contributions. In response, this paper proposes a digital-twin-based efficient energy control framework that integrates meta-learning with resource-aware model selection. The system collects energy consumption data, extracts statistical features--including trends, seasonality, skewness, and kurtosis--to generate meta-features, and characterizes candidate models by their complexity, performance, and execution time. By combining these meta-features and model profiles, the proposed framework dynamically selects the optimal forecasting model that satisfies user requirements while adapting to available computing resources.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Automation Level** & **Real-Time Latency** & **Resource Awareness** \\ \hline
[14] & Manual rule based & X (Not supported) & X \\ \hline
[16,17] & Fully automated & X & X \\ \hline
[15,18,19,20] & Automated & \(\triangle\) (Partially supported) & X \\ \hline
[21] & Semi-automated & X & X \\ \hline The proposed framework & Fully automated & O (Fully supported) & O \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the existing methods and the proposal.