Fog Paradigm for Local Energy Management Systems

Abstract

Cloud Computing infrastructures have been extensively deployed to support energy computation within built environments. This has ranged from predicting potential energy demand for a building (or a group of buildings), undertaking heat profile/energy distribution simulations, to understanding the impact of climate and weather on building operation. Cloud computing usage in these scenarios have benefited from resource elasticity, where the number and types of resources can change based on the complexity of the simulation being considered. Many such energy simulations require resource scalability and on-demand resources to meet the computational and data storage requirements. While there are numerous advantages of using a cloud based energy management system, there are also significant limitations. For instance, many such systems assume that the data has been pre-staged at a cloud platform prior to simulation, and do not take account of data transfer times from the building to the simulation platform.The need for supporting computation at edge resources, which can be hosted within the building itself or shared within a building complex, has become important over recent year. Additionally, network connectivity between the sensing infrastructure within a built environment and a data centre where analysis is to be carried out can be intermittent or may fail. There is therefore also a need to better understand how computation/analysis can be carried out closer to the data capture site to complement analysis that would be undertaken at the data centre. We describe how the Fog computing paradigm can be used to support some of these requirements, extending the capability of a data centre to support energy simulation within built environments.

Scheduling Real-Time Security Aware Tasks in Fog Networks

“…Additionally, where the network connecting a user device to a cloud data center can fail or have a variable availability profile (i.e. network Quality of Service can change significantly over time, in unpredictable ways), edge resources can either: (i) support an approximate version of capability that would be carried out within a data center [30], or (ii) enable adaptation of a pre-generated model to be carried out [27], enabling subsequent re-synchronisation of this model with the cloud once the network connection is re-established. The use of edge resources also has a bearing on issues around data ownership and trust in a cloud data center provider, as data shared with a cloud provider can be directly viewed and searched.…”

Fog BEMS: An Agent-Based Hierarchical Fog Layer Architecture for Improving Scalability in a Building Energy Management System

“…Khalid et al [41] investigated a fog-based approach to share energy among consumers, where a fog server enables the consumers to communicate each other. Javed et al [42] addressed the case of communication failure between a cloud BEMS and endpoints (e.g., electric vehicles). They proposed installing a forecasting model at the edge of the network so that endpoints could operate as expected, even in the case of failure.…”

A survey on cloud computing in energy management of the smart grids

“…A survey on the conventional and emerging energy management strategies consisting of the cloud computing technique, demand response programs, real-time control, and multi agent systems in the smart grid. 35…”