Full length article A multidimensional data model design for building energy management José Cavalheiro, Paulo Carreira ⇑ INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Portugal article info Article history: Received 20 May 2016 Received in revised form 30 July 2016 Accepted 15 August 2016 Keywords: Energy management Energy metering Data warehousing Decision support Multidimensional model abstract Data organization is a critical aspect in Building Energy Data Management. Yet, despite the importance of the topic, no sound reference model for energy data has been proposed in the literature that has been developed according to well-founded methodologies. This article proposes a reference data model devel- oped according to standard multidimensional modeling methodologies and improved iteratively in review meetings with expert users (in the building energy management domain). The quality of the model is evaluated according to complexity, usability, and design metrics thus achieving a high- quality re-usable multidimensional data model that can be applied to create or improve on the data model designs of building energy management systems. Ó 2016 Elsevier Ltd. All rights reserved. 1. Introduction To find energy-saving opportunities, energy consumption data must be analyzed in the light of the factors that influence it. In buildings, this means analyzing energy consumption in terms of multiple dimensions such as the arrangement of spaces, the speci- ficities of constructive elements, the characteristics of the installed/commissioned equipment, and, ultimately, the behavior of the occupants [1–5]. Decision Support Systems (DSSs) are well-known in manage- ment for supporting this sort of multidimensional analysis [6], enabling managers to choose among different courses of action [7, pp. 21–50]. In particular, database-oriented DSSs collect data from multiple sources and store it in a data repository such as a Data Warehouse (DW) [7, pp. 207–208]. In this repository, data is organized under a global unified schema that facilitates data analysis and presentation [8]. This reference schema, commonly known as a multidimensional model [9], is encoded using the core concepts of fact and dimension tables. Facts are observations regarding the business performance, and dimensions are the set of attributes that describe the business measurements [10]. Using a multidimensional model, distinct tools are able to cooperate, enabling managers to integrate, analyse and visualize large vol- umes of data—a degree of separation of concerns that largely explains the success of DSSs. Building automation systems (BAS) are a class of systems that monitor and control buildings. However, they often lack the ability to provide users feedback on how energy is spent. Towards this aim, several authors propose integration of BASs with database ori- ented DSS. In the context of this article we will refer collectively to these integrated systems as a Building Energy Management System (BEMS) [11–13]. As this article will make clear, BEMS comprise such DSS activities as (i) consolidating energy-related data from different sources, (ii) supporting the use of data access tools to analyse building performance, (iii) visualizing energy-related data, and (iv) generating reports [14,13]. All these activities must access the DSS database data model. Although the creation of multidimensional models is, by now, well established in the information systems domain [9,8], creating a reference multidimensional model for energy management deci- sion support is challenging. The explanation for this fact lies in the difficulty to obtain precise detailed requirements regarding energy management activities. First, the existing energy management standards such as I. S. 393:2005 [15], ANSI/MSE 200:2008 [16], BS EN ISO 16001:2009 [17], and BS EN ISO 50001:2011 [18] do not agree on precise business requirements for energy management. Second, these standards do not deliver appropriate detail to enable deriving accurate information requirements [19]. In addition, it is well known that business process systematization is essential to obtain an accurate model formulation, without which many formu- lations are possible, but are either incomplete or inaccurate, thus resulting in increased development and maintenance costs [20]. The lack of a quality information model can be grasped in BEMSs with confusing user interfaces (that force users to discard large amounts of data [12,21]), and are limited in terms of analysis http://dx.doi.org/10.1016/j.aei.2016.08.001 1474-0346/Ó 2016 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. E-mail addresses: jose.cavalheiro@tecnico.ulisboa.pt (J. Cavalheiro), paulo.car- reira@tecnico.ulisboa.pt (P. Carreira). Advanced Engineering Informatics 30 (2016) 619–632 Contents lists available at ScienceDirect Advanced Engineering Informatics journal homepage: www.elsevier.com/locate/aei