## 1 Introduction
Additionally, predictions may not be available when the network connection is down between cloud and edge domains (e.g., building in a rural area), and the prediction model is executed in the cloud domain. The downside of placing inference functionality at edge nodes is their lower performance in terms of computing power (CPU/GPU) and memory. Both approaches have privacy concerns regarding the communicated information (prediction model, predictions) between the domains. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cloud** & **Edge** \\ \hline \multirow{2}{*}{**Communication**} & Predictions have to be constantly transferred to the edge-nodes. & Model updates are transferred periodically to edge nodes. \\ \hline \multirow{2}{*}{**Privacy concerns**} & Communicated predictions on building’s energy consumption & Communicated updates to the prediction model. \\ \hline \multirow{2}{*}{**Independency of operation**} & The solution is dependent on the predictions provided from the cloud domain. & The latest model may be used for predicting energy consumption in the edge node, when network connection is down. \\ \hline \multirow{2}{*}{**Energy consumption concern in edge nodes**} & Increased energy consumption caused by transfer of predictions. & Transfer of model updates to the edge nodes. \\  & High performance CPU/GPU & Execution of prediction model. \\ \hline \multirow{2}{*}{**Processing performance**} & \multirow{2}{*}{
\begin{tabular}{c} High performance CPU/GPU \\ (thousands of CUDA cores) \\ \end{tabular} } & Low performance CPU/GPU \\  & (thousands of CUDA cores) & (up to hundreds CUDA cores) \\ \hline \multirow{2}{*}{**Memory**} & High (up to hundreds GBs) & Low (few GBs) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between placing prediction/inference functionality in cloud or edge domains. Cloud = prediction model is executed in cloud domain, and predictions are transferred to edge nodes; Edge = prediction model is transferred from the cloud domain to edge nodes for prediction. While both approaches may be feasible for design and deployment of an energy management system, in this paper model, training and deployment is centralised to the cloud domain while placing inference functionality to the edge domain. The argued benefits of edge inference (vs. cloud inference) is better scalability in terms of the amount of communication needed (constant transfer of predictions is not needed for each building), which may also lead to lower energy consumption at edge nodes (network access is needed less frequently), and independency of operation during a possible network outage. Particularly, a goal is to design and evaluate a distributed computing system architecture, where software-, data- and ML-engineering activities (DevOps [14], ModelOps [15], CI/CD) are centrally supported by cloud computing, but ML-based predictive models are deployed and managed as software for continuous forecasting of building-specific energy consumption at edge computing environments. Various Reference Architectures (RA) have been designed for edge computing environments for facilitating the design of concrete architectures [16; 17]. Also, many choices have been explored for increasing the performance of Machine learning (ML)-based inference in cloud-edge continuum [12]. Further, technologies (e.g., Rancher [18], k3s [19]) have been developed for management of services in cloud and edge computing environments. Finally, energy consumption prediction in power plants has been studied earlier [20; 21]. However, architecture design in cloud-edge continuum, focusing on the integration between service management technologies for building energy modelling, has not been studied according to the authors' best knowledge. The contribution of this paper is an architecture in cloud-edge continuum focusing on building's heat demand forecasting (energy consumption prediction) in buildings. The research was performed based on Design Science Research Methodology (DSRM) [22].