Real-Time Integration of Building Energy Data

Abstract

Abstract-An Energy Management System (EMS) is a monitoring tool that tracks buildings energy consumption with the purpose of enhancing energy efficiency, by identifying savings opportunities and misuse situations. To achieve this, EMSs collect data flowsdata streams-from a network of energy meters and sensors, which are then combined into useful information. Data must be processed in real-time, to support a timely decision making process. I. IntroductionBuildings account for 40% of energy consumption, ahead of other sectors, such as industry or transportation [1]. Therefore, small improvements on building energy consumption translate to major savings. Among other ways, energy efficiency in buildings can be achieved through Intelligent Energy Management [2]. This topic refers to monitoring energy consumption, and the careful tracing of energy usage enabling building managers to identify saving opportunities. EMSs continuously monitor the energy consumed in buildings. Consumption related data is evaluated according to several variables such as time, areas and their occupation, equipment state, expected consumption, among others, which determines the building energy usage patterns, providing required information to determine the adjustments towards improving energy usage [3].One fundamental aspect of energy management is timeliness: faster decisions translate to less waste and larger savings. In other words, timely information greatly improves the decision making process [4] since building managers are able to immediately diagnose and promptly respond to anomalous situations. EMSs are real-time decision making applications that require (near) real-time integration of huge quantities of data, wherein each record relates to a very short period [5], thus leading to Big Data challenges: (i) achieve low latency on queries evaluation,

Energy Consumption Prediction with Big Data: Balancing Prediction Accuracy and Computational Resources

“…Frincu et al [25] and Anjos et al [26] have been concerned with Big Data in the energy sector. Frincu et al proposed an approach for selecting the prediction model, whereas Anjos et al took a streaming approach to energy management.…”

Energy Signature Analysis: Knowledge at Your Fingertips

“…Proposed solutions are general purpose [17] or tailored to a given application domain, such as thermal energy consumption [18], residential energy use [19], renewable energy [20], air pollution levels [21]. Authors in [17] highlight the key features that should be included in an analytics cloud service.…”

“…The work in [18] tries to point out the key features of an Energy Management System, to support frequent pattern discovering on event streams. A Data Stream Management System (DSMS) is used, to better suit the typical queries of real-time EMSs on time-varying data streams.…”

An Energy Management Service for the Smart Office

“…Methods for knowledge extraction to automatically infer and adapt rule sets for the management of a smart office (or a generic smart building) are proposed by Gupta et al [20] and Anjos et al [21]. By analyzing data gathered from electricity meters, sensors and actuators deployed within the office environment and combining them with the users' preferences, control rules can be dynamically generated, modified and deleted.…”