• Energy Research

Integrating data analytics, optimisation and dynamic control to support energy services has seen significant interest in recent years. Larger appliances used in an industry context are now provided with Internet of Things (IoT)-based interfaces that can be remotely monitored, with some also provided with actuation interfaces. The combined use of IoT and edge computing enables connectivity between energy systems and infrastructure, providing the means to implement both energy efficiency/optimisation and cost reduction strategies. We investigate the economic implications of harnessing IoT and edge/cloud technologies to support energy management for HVAC (Heating, Ventilation and Air Conditioning) systems in buildings. In particular, we evaluate the cost savings for building operations through energy optimisation. We use a real use case for energy optimisation as identified in the EU “Sporte2” project (focusing on the energy optimisation of sports facilities) and explore several scenarios in relation to costs and energy optimisation.

A Local Energy Management System (LEMS) is described to control Electric Vehicle charging and Energy Storage Units within built environments. To this end, the LEMS predicts the most probable half hours for a triad peak, and forecasts the electricity demand of a building facility at those times. Three operational algorithms were designed, enabling the LEMS to (i) flatten the demand profile of the building facility and reduce its peak, (ii) reduce the demand of the building facility during triad peaks in order to reduce the Transmission Network Use of System (TNUoS) charges, and (iii) enable the participation of the building manager in the grid balancing services market through demand side response. The LEMS was deployed on over a cloud-based system and demonstrated on a real building facility in Manchester, UK.

A model-based approach is described to forecast triad periods for commercial buildings, using a multi-staged analysis that takes a number of different data sources into account, with each stage adding more accuracy to the model. In the first stage, a stochastic model is developed to calculate the probability of having a “triad” on a daily and half-hourly basis and to generate an alert to the building manager if a triad is detected. In the second stage, weather data is analysed and included in the model to increase its forecasting accuracy. In the third stage, an ANN forecasting model is developed to predict the power demand of the building at the periods when a “triad” peak is more likely to occur. The stochastic model has been trained on “triad” peak data from 1990 onwards, and validated against the actual UK “triad” dates and times over the period 2014/2015. The ANN forecasting model was trained on electricity demand data from six commercial buildings at a business park for one year. Local weather data for the same period were analysed and included to improve model accuracy. The electricity demand of each building on an actual “triad” peak date and time was predicted successfully, and an overall forecasting accuracy of 97.6% was demonstrated for the buildings being considered in the study. This measurement based study can be generalised and the proposed methodology can be translated to other similar built environments

As cloud computing becomes the dominant mechanism for delivery of electronic services, significant recent effort has focused on certifying cloud services to ensure their compliance with security and privacy standards (such as GDPR). Assessing the benefit of using a particular cloud service, especially if such a service is being offered by providers that may be new to the cloud marketplace, remains a challenge. Cloud Security Alliance (CSA) provides a certification approach that associates a ranking to providers based on their capability assessment using a “cloud control matrix”. A provider can make a self-assessment, or an assessment can be undertaken by a third party. The intention is to increase user trust in a provider based on their rating using this methodology. This work investigates whether a similar certification methodology can be applied to edge resources, especially if these edge resources are combined with cloud services in a “smart cities” context. Can a CSA-like approach also be used to increase trust in use of edge resources? How would the CSA methodology need to change to support this type of assessment, and how useful is such an approach likely to be in practice? We propose a risk assessment methodology that can be used to address these concerns, and evaluate it in a practical application using both edge and cloud computing resources.

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.<br>

Automatic coordination mechanisms for the grid are required due to the increasing complexity exhibited in large scale distributed systems. Decentralized economic models are being considered as scalable coordination mechanisms for the management of service allocations to clients. However, decentralization incorporates further dynamicity and unpredictability into the system. Introducing higher levels of adaptation and learning in the coordination protocols helps cope with complexity. We provide a solution based on a self-organized, emergent mechanism evolving grid market participants through a group selection process. Dynamic congregations organize agents into optimized market segments, maximizing utility and thereby improving system-wide performance. We provide a system model and evaluation by simulation of the group selection mechanism. We further provide a prototype showing practical feasibility of the approach.

Commercial buildings have been identified as a major contributor of total global energy consumption. Mechanisms for collecting data about energy consumption patterns within buildings, and their subsequent analysis to support demand estimation (and reduction) remain important research challenges, which have already attracted considerable work. We propose a cloud based energy management system that enables such analysis to scale to both increasing data volumes and number of buildings. We consider both energy consumption and storage to support: (i) flattening the peak demand of commercial building(s); (ii) enable a “cost reduction” mode where the demand of a commercial building is reduced for those hours when a “triad peak” is expected; and (iii) enables a building manager to participate in grid balancing services market by means of demand response. The energy management system is deployed on a cloud infrastructure that adapts the number of computational resources needed to estimate potential demand, and to adaptively run multiple what-if scenarios to choose the most optimum configuration to reduce building energy demand.