Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.

Cross-sectional energy benchmarking in the building domain has become crucial for policymakers, energy managers and property owners as they can compare an immovable property performance against its closest peers. For this, Key Performance Indicators (KPIs) are formulated, often relying on multiple and heterogeneous data sources which, combined, can be used to set benchmarks following normalization criteria. Geographically delimited parameters are important among these criteria because they enclose entities sharing key common characteristics the geometrical boundaries represent. Linking georeferenced heterogeneous data is not trivial, for it requires geographical aggregation, which is often taken for granted or hidden within a pre-processing activity in most energy benchmarking studies. In this article, a novel approach for Linked Data (LD) generation is presented as a methodological solution for data integration together with its application in the energy benchmarking use case. The methodology consists of eight phases that follow the best principles and recommend standards including the well-known GeoSPARQL Open Geospatial Consortium (OGC) for leveraging the geographical aggregation. Its feasibility is demonstrated by the integrated exploitation of INSPIRE-formatted cadastral data and the Buildings Performance Certifications (BPCs) available for the Catalonia region in Spain. The outcomes of this research support the adoption of the proposed methodology and provide the means for generating cross-sectional building energy benchmarking histograms from any-scale geographical aggregations on the fly.

The Cross-sectional buildings energy benchmarking knowledge graph is a dataset defined within the framework of one of the Building Information aGGregation, harmonisation and analytics platform (BIGG) Horizon 2020 project’s Business Cases. It is a combination of Semantic Web assets whose main objective is integrating cadastral data, following the Infrastructure for Spatial Information in Europe (INSPIRE) standard, with Building Performance Certifications (BPCs). The dataset can be exploited to provide valuable information for politicians, energy managers and property owners, e.g. to set clear objective zones in the formulation of subsidy policies, implement localized energy efficiency measures, or compare an owned property against its peer group benchmark in terms of energy efficiency. When published using a GeoSPARQL-enabled endpoint, a data consumer can perform on-the-fly geographical and KPIs aggregations with valuable outputs such as heat maps, frequency distributions, and more.

This paper is related to the H2020 project EN-TRACK, dedicated to developing a platform for gathering data on the performance of energy efficiency investments in buildings. The project aims to collect and harmonize data from different sources and provide services supporting investors and building owners in decision-making and de-risking building retrofit projects. The paper focuses on the methodology and the semantic technologies used in the development of the platform’s data model, which enables the interoperability of data, and supports the service functionalities for tracking building energy performance and benchmarking savings from energy efficiency investments.