Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

Demand flexibility enabled by virtual energy storage to improve renewable energy penetration

The increasing resort to renewable energy distributed generation, which is needed to mitigate anthropogenic CO2 emissions, leads to challenges concerning the proper operation of electric distribution systems. As a result of the intrinsic nature of Renewable Energy Sources (RESs), this generation shows a high volatility and a low predictability that make the balancing of energy production and consumption difficult. At the same time, the electrification of new energy‐intensive sectors (such as heating) is expected. This complex scenario paves the way for new sources of flexibility that will have more and more relevance in the coming years. This paper analyses how the electrification of the heating system, combined with an electric flexibility utilisation module, can be used to mitigate the problems related to the fluctuating production of RES. By using Power‐to‐Heat (P2H) technologies, buildings are able to store the overproduction of RES in the form of thermal energy for end‐use according to the principle of the so‐called Virtual Energy Storage (VES). A context‐aware demand flexibility extraction based on the VES model and the flexibility upscale and utilisation on district‐level through grid simulation and energy flow optimisation is presented in the paper. The involved modules have been developed within the PLANET (PLAnning and operational tools for optimising energy flows and synergies between energy NETworks) H2020 European project and interact under a unified co‐simulation framework with the PLANET Decision Support System (DSS) for the analysis of multi‐energy scenarios. DSS has been used to simulate a realistic future energy scenario, according to which the imbalance problems triggered by RES overproduction are mitigated with the optimal exploitation of the demand flexibility enabled by VES

A decision support system for fostering smart energy efficient districts

The role of ICT is becoming prominent in tackling some of the urban societal challenges such as energy wastage and increasing carbon emissions. In this context, the concept of DAREED aims to deliver an integrated decision support system (DSS) to drive energy efficiency and low carbon activities at both a building and district level. The main aim of this paper is to present the technical concept of the Best Practices recommendation component of the DAREED system. This component seeks to compare and identify existing best practices to recommend practical actions to various stakeholders (e.g. building managers, citizens) in order to improve energy performance considering the global needs of a building. This paper also discusses the context of the three field trial sites (based in UK, Spain and Italy) in which the DAREED platform along with the best practices tool is to be tested and validated.This work evolved in the context of the project DAREED (Decision support Advisor for innovative business models and useR engagement for smart Energy Efficient Districts), www.dareed.eu, a project cofunded by the EC within FP7, Grant agreement no: 609082

A GAMIFICATION FRAMEWORK FOR CUSTOMER ENGAGEMENT AND SUSTAINABLE WATER USAGE PROMOTION

The recent advent of smart meters to increase the effectiveness of urban Water Demand Management Strategies (WDM) has allowed water utilities to gather quasi real-time consumption data to monitor the network status and load and useful to develop models of consumers' behavior. At the same time, the consumption information can warn users about their habits in a fine-grained way. In principle, the feedback alone could stimulate increased awareness on water usage, but the motivations and individual attitudes of consumers are mostly hidden. Moreover, the same sustainable behavior should be incentivized also for households in which smart metering solutions are not present, but for which data gathering becomes a challenge. Modifying users' behavior by means of software is a tough task, due to the difficulty in designing an effective application able to maintain the behavioral changes in the long term. Gamification, the use of game design techniques and game mechanics to enhance traditional applications and drive behaviors of its users, has been proven successful in tackling with the problem. In this work, we propose a gamified application to enhance users' participation and data gathering in a real WDM scenario, by describing the designing principles and the architecture of the envisioned solution. An integrated approach exploiting both board and digital games to incentivize users to submit meaningful data for water utilities and change their long-term behavior is also detailed. The work is part of the SmartH2O project, which aims at creating an ICT platform to raise customers' awareness about their consumption and pursue water savings in the residential sector

Meter Scoping Study

This report presents a summary of metering technology and cost information from past studies in an attempt to identify key barriers to more widespread implementation