###### Contents  
* 1 Introduction
* 2 As global energy demand intensifies across industrial, commercial, and residential domains, achieving accurate and efficient energy management has become a critical priority [1, 2]. In response, Energy Digital Twins (EDTs) have emerged as promising tools for real-time monitoring and control of energy systems, offering data-driven monitoring, prediction, and optimization insights through virtual replicas of physical infrastructures [3, 4, 5]. These systems rely on time-series forecasting models to predict energy usage, demand/ supply, sensor operation monitoring, malfunction detection, and the optimization of energy control operations [6]. However, the effectiveness of EDTs depends not only on the accuracy of forecasting models but also on their ability to operate within resource-constrained computing environments [7]. Modern time-series forecasting models span a wide range of complexity--from lightweight statistical approaches such as ARIMA and Prophet to computationally heavy deep-learning-based models such as the CNN, LSTM, GRU, and Transformer [8; 9; 10; 11]. While advanced models often achieve higher accuracy, they typically require substantial computational resources and longer inference times, which are problematic in edge computing environments [12]. Edge computing, which processes data near the source to minimize latency and bandwidth, is particularly relevant in energy systems where immediate forecasting and control are crucial. On the other hand, deploying complex models across diverse edge devices with limited CPU and memory availability introduces a trade-off between accuracy and responsiveness [13]. Consequently, selecting the appropriate forecasting model in resource-constrained environments becomes essential. The previous methods proposed diverse model selection schemes for accurate and efficient time-series services [14; 15; 16; 17; 18; 19; 20; 21]. Some of the traditional methods are exhaustive search methods, which evaluate all candidate models to identify the optimal one, achieving high selection accuracy. AutoML platforms, such as AutoGluon and AutoKeras, automate the model selection process and reduce human intervention by leveraging hyperparameter optimization and architecture search. The automated solutions can identify the best-performing models. Other approaches include meta-learning-based model selection methods that train a meta-learner with the characteristics of time series, such as the length, skewness, kurtosis, seasonality, and trend, for adaptability and generalization on new tasks. However, these methods assume abundant computational resources, which makes them difficult to simultaneously achieve both service responsiveness and high prediction accuracy. In real-world scenarios, particularly in edge-based energy management systems, concurrent requests from multiple energy control service users need to be handled. When computational resources are disproportionately allocated--either favoring high-complexity models or spreading too thin across many low-resource tasks--this can result in load imbalance, degraded prediction performance, and increased service response times. Furthermore, dynamic fluctuations in available resources, caused by factors such as hardware limitations or concurrent processing demands, can disrupt initial predictions of system capacity, exacerbating resource contention and compromising both accuracy and responsiveness in time-sensitive energy forecasting services. The main contributions of this paper are as follows:  
* A two-stage model-selection scheme: (i) A decision-tree meta-learner trained on 22 statistical meta-features proposes a candidate forecasting model, and then (ii) a KNN-based similarity correction refines that choice, improving selection accuracy without exhaustive search. * A configurable runtime fallback that replaces the meta-learner's pick with a lower-complexity model whenever its predicted inference time exceeds the current CPU quota or concurrency budget, ensuring bounded latency on edge devices.