## 3 Research Method and Process  
This research applies the existing Design Science Research Methodology (DSRM) [22]. The research process has been illustrated in Figure 1. The DSRM research process consists of six activities [22]. Initially, a research problem is defined, and the value of a solution is described and motivated. Subsequently, objectives of a solution are specified. Then, an artifact is designed and developed. The artifact can be utilised for demonstrating the solving of the research problem. Evaluation focuses on comparing objectives of a solution to the results obtained with the developed artifact(s) in use. Different evaluation criteria can be used for performing ex-post evaluations [45]. At the end of the evaluation activity, the researcher may iterate back to the design and development activity for improving the performance of the solution. Finally, the problem, artifact(s), and results are communicated to the research community (typically in publications). In the following, our research applying the DSRM is explained in detail.  
The initial problem originated from the need to realise energy consumption prediction for building energy management, which should ultimately enable cost savings for building owners the main research problem was:  
* What kind of architecture enables training, transfer, deployment, and inference of ML-based model(s) in cloud-edge computing continuum for energy usage prediction?  
Due to the benefits of placing inference in close proximity of buildings (Table 1), an objective was defined for designing, developing, and evaluating an architecture, which enables inference functionality in edge nodes while training the model(s) in the cloud domain. We focused on enabling predictions at edge nodes, and left optimisation of energy consumption for future work. Subsequently, we defined additional objectives for the architecture. First, the architecture should enable continuous training (CI/CD) and switching of models in cloud-edge continuum, which should not lead to interruptions in the prediction service. Model switching is required due to concept drift, which may cause inaccurate predictions with outdated models. Additionally, management and deployment of models should be automatic. Latency of predictions should be less than 1 min, because  
Figure 1: Our study was performed based on the Design Science Research Methodology [22].  
the frequency of the best smart energy meters has been 1 min [46]. Finally, the architecture should be vendor-independent to avoid locking into company-specific offerings. Our research was objective-centred, because the objectives drove our research process.  
The objectives triggered design and development, demonstration, and evaluation phases of the research. Especially, three different architectural approaches were designed, developed, and evaluated regarding feasibility before reaching to the final version of the architecture. Existing knowledge regarding RA for edge computing systems [17], a Stacked Boosters Network-model [23], and service management technologies (Rancher, k3s, Docker) were utilised in the design and development of the architecture. The implemented architectural approaches based on SBN-model and service management technologies can be considered as DSRM-artifacts [22] for solving the research problem. Energy consumption prediction was used as a use case for demonstration and evaluating architectural approaches in a laboratory setting.  
Performance of the final architecture version was tested in terms of prediction and model switching latency in edge nodes for evaluation of efficiency. Additionally, the architecture was evaluated in terms of different ex-post criteria [45] (feasibility, efficiency, fidelity with real-world phenomena, operationality of the architecture).