Methods for comparing digital applications in buildings and districts

Abstract

The so-called Energiewende is a complex task with a variety of stakeholders, regulations, technical infrastructure, and proposed solutions. Buildings are an important sector for increasing resource and energy efficiency, as in Germany around 35 % of end energy usage can be attributed to them. Digital applications can help reduce these emissions through more efficient planning, operating, renovation, or demolition. Depending on the task and the parties involved, the complexity of descriptions and data models can vary from simple energy efficiency labels used in the labeling process of buildings to complex individual data models used for simulation. However, these specific information systems are often non-transferable, are hard to compare in their restrictions and requirements, and hence increase the overall costs of digital solutions. A good understanding is needed to preserve economic and ecological benefits while maintaining privacy and security aspects. To compare digital applications a variety of solutions are identified, namely frameworks, taxonomies, and ontologies. While Frameworks focus more on technological aspects and are complex to use, they provide an in-depth understanding. Taxonomies can be used for a less infrastructure-dependent comparison and provide simple relationships. Ontologies provide relationships and definitions while being task-dependent. We conclude that comparing the underlying data models of digital applications is a complex task and dependent on the application and its infrastructure. However, a variety of tasks refers to the same tools and data. After discussing these approaches, we then give an overview of digital applications developed by German researchers. Last we give an insight on how to combine these aspects in our ongoing research. To summarize, in this paper we give an overview of the complexity involved in transferring digital solutions in the building sector, provide a method used for comparing applications and describe a solution to compare infrastructure and digital tools built for it.

How Can Non-Intrusive Load Monitoring Contribute to the Assessment of the Smart Readiness Indicator?

“…There is a variety of ways in which a digital application pursuing a specific goal can be described or developed [17]. This makes a general evaluation of digital applications unattainable.…”

“…If the reporting is not in relation to a person and their actions, or not comprehensible to them, it does not bring added value to the service. Benchmarks and feedback should be comparable and understandable [20]. If the frequency or spatial aggregation is not appropriate, e.g., in case of large buildings, or the given statements are wrong, e.g., in case of false or mismatching feedback and benchmarks, the service might not fulfill its duties.…”

Understanding resource consumption and sustainability in the built environment

“…Qualitative data include interviews and contextual observations to inform different analyses. Together, these data reflect the specific built environment context, and this context specificity motivates further data collection and sharing [7]. In collecting and sharing data, however, there are often tradeoffs between data spatiotemporal resolution (and its implications for privacy, data gathering, and management costs), monitoring locations, and study duration [8][9][10][11][12].…”

“…Various data gathering, handling (e.g., dimensionality reduction), and modeling approaches are represented in this focus issue, including regression [3] and other machine learning methods for classification [10,11], principal component analysis [12,13], participatory action research [8], differential equations representing underlying physical science [14], and other building-scale models [14][15][16]. Research models and analyses, combined with multi-scale digital applications [7], can translate impacts from large-scale infrastructure operations (e.g., CO 2 emissions from electricity generation) to the end-use scale [6,16], though there are often overlapping spatial scales for resources and governance. Computational tradeoffs exist between model dimensionality and model skill [12].…”

District Energy Management Systems: Key Data Points for System Integration and Related Challenges: Lessons Learned from Experts in Germany

“…[20,21] It is noteworthy that many of the projects face similar challenges regarding data availability, interfaces, or reusability of results, methods, and data. [22] This leads to the main motivation of this article: to provide a structured approach to help support the planning, operation, and evaluation of DEMS and the respective underlying data model. While the planning and operation of newly built DEMS is possible in Germany e.g., ref.…”