Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO 2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.

A review of data-driven building energy consumption prediction studies

Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

Founded in 1961 to replace the Organisation for European Economic Cooperation (OEEC), which was linked to the Marshall Plan and was established in 1948. The change of title marks the Organisation’s altered status and functions: with the accession of Canada and USA as full members, it ceased to be a purely European body, and at the same time added development aid to the list of its priorities. The aims of the organization are to promote policies designed to achieve the highest sustainable economic growth and employment and a rising standard of living in Member countries, while maintaining financial stability, and thus to contribute to the development of the world economy; to contribute to sound economic expansion in Member as well as non-member countries in the process of economic development; and to contribute to the expansion of world trade on a multilateral, nondiscriminatory basis in accordance with international obligations.

Organization for Economic Co-Operation and Development (OECD)

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

https://uwe-repository.worktribe.com/preview/909609/1.%20ADVEI-D-16-00028R2.pdf

Big Data in the construction industry

Stakeholder management for public private partnerships

Automated regulatory compliance checking requires automated extraction of requirements from regulatory textual documents and their formalization in a computer-processable rule representatio...

Semantic NLP-Based Information Extraction from Construction Regulatory Documents for Automated Compliance Checking

With the increasing demands for domain-wide integrated construction and infrastructure development, there is a need for developing a domain ontology to build a knowledge model that describes the multistakeholder project development process. This paper presents a domain ontology for supporting knowledge-enabled process management and coordination across various stakeholders, disciplines, and projects. The ontological model is composed of concepts, relations, and axioms. Concepts represent the "things" in the domain of interest; relations establish the interconcept links; and axioms specify the definitions of concepts and relations and constraints on their behavior and interpretation. The ontology models the most fundamental concepts in the domain in a structured, extendable, and flexible format to facilitate future evolution and extension of the ontology for representing application-specific and/or enterprise-specific knowledge. The ontology was evaluated through technical evaluation and user evaluation. User evaluation was conducted through one-to-one expert evaluation interviews.

Nora El-Gohary

Papers

A review of data-driven building energy consumption prediction studies

Stakeholder management for public private partnerships

Semantic NLP-Based Information Extraction from Construction Regulatory Documents for Automated Compliance Checking

Domain Ontology for Processes in Infrastructure and Construction

Integrating semantic NLP and logic reasoning into a unified system for fully-automated code checking