Established in 2006, TATA Africa Services (Nigeria) Limited operates as the nodal point for Tata businesses in West Africa. TATA Africa Services (Nigeria) Limited has a strong presence in Nigeria with investments exceeding USD 10 million. The company was established in Lagos, Nigeria as a subsidiary of TATA Africa Holdings (SA) (Pty) Limited, South Africa and serves as the hub of Tata’s operations in Nigeria and the rest of West Africa.

What About Us

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

A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends

Towards a semantic Construction Digital Twin: directions for future research

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.

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Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

The energy landscape is experiencing accelerating change; centralized energy systems are being decarbonized, and transitioning towards distributed energy systems, facilitated by advances in power system management and information and communication technologies. This paper elaborates on these generations of energy systems by critically reviewing relevant authoritative literature. This includes a discussion of modern concepts such as ‘smart grid’, ‘microgrid’, ‘virtual power plant’ and ‘multi-energy system’, and the relationships between them, as well as the trends towards distributed intelligence and interoperability. Each of these emerging urban energy concepts holds merit when applied within a centralized grid paradigm, but very little research applies these approaches within the emerging energy landscape typified by a high penetration of distributed energy resources, prosumers (consumers and producers), interoperability, and big data. Given the ongoing boom in these fields, this will lead to new challenges and opportunities as the status-quo of energy systems changes dramatically. We argue that a new generation of holonic energy systems is required to orchestrate the interplay between these dense, diverse and distributed energy components. The paper therefore contributes a description of holonic energy systems and the implicit research required towards sustainability and resilience in the imminent energy landscape. This promotes the systemic features of autonomy, belonging, connectivity, diversity and emergence, and balances global and local system objectives, through adaptive control topologies and demand responsive energy management. Future research avenues are identified to support this transition regarding interoperability, secure distributed control and a system of systems approach.

Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources

Usability evaluation of a web-based tool for supporting holistic building energy management

Integrating building and urban semantics to empower smart water solutions

Power distribution network management must integrate with demand side management, alongside distributed energy resources, in order to meet sustainability, resilience, and economic challenges through a smart grid approach. This paper presents an implementation of the Universal Smart Energy Framework (USEF) through a multiagent system and a novel semantic web ontology, which aligns and enriches relevant existing standards. USEF provides a common specification of the market processes and information exchange but does not specify the internal reasoning of the different roles involved. The authors explain the systematic design and development process from the requirements of the energy-flexibility value chain to software implementation. The underpinning ontology formalizes a domain perspective which is coherent with existing standards, and is sufficient for the agent-oriented implementation of the mentioned framework. As well as contributing this model as a web ontology artifact, the presented work utilizes metaprogramming to transform the domain model into a standard agent communication language ontology. The research reported in this paper is expected to lead towards efficient and scalable development of decision support and automation software for smart grids.

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Shaun Howell

Papers

Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources

Usability evaluation of a web-based tool for supporting holistic building energy management

Integrating building and urban semantics to empower smart water solutions

Ontology-based demand-side flexibility management in smart grids using a multi-agent system

Ontology-driven development of web services to support district energy applications