Data Ingredients: smart disclosure and open government data as complementary tools to meet policy objectives. The case of energy efficiency.

Open government data are considered a key asset for eGovernment. One could argue that governments can influence other types of data disclosure, as potential ingredients of innovative services. To discuss this assumption, we took the example of the U.S. 'Green Button' initiative – based on the disclosure of energy consumption data to each user – and analysed 36 energy-oriented digital services reusing these and other data, in order to highlight their set of inputs. We find that apps suggesting to a user a more efficient consumption behaviour also benefit from average retail electricity cost/price information; that energy efficiency 'scoring' apps also need, at least, structured and updated information on buildings performance; and that value-added services that derive insights from consumption data frequently rely on average energy consumption information. More in general, most of the surveyed services combine consumption data, open government data, and corporate data. When setting sector-specific agendas grounded on data disclosure, public agencies should therefore consider (contributing) to make available all three layers of information. No widely acknowledged initiatives of energy consumption data disclosure to users are being implemented in the EU. Moreover, browsing EU data portals and websites of public agencies, we find that other key data ingredients are not supplied (or, at least, not as open data), leaving room for possible improvements in this arena

Optimisation of a water company’s waste pumping asset base with a focus on energy reduction

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWater companies use a significant quantity of electricity for the operation of their clean and wastewater assets. Rising energy prices have led to higher energy bills within the water companies, which has increased operating costs. Thus, improvements in demand side energy management are needed to increase efficiency and reduce costs, which forms the premise for this research project. Thames Water Utilities Ltd has identified that improvements in demand side energy management is required and is currently researching various methods to reduce energy consumption. One initiative included the upgrade of a variety of site telemetry assets. By deploying these new telemetry assets, Thames Water Utilities Ltd are more able to liberate the asset data and as such, be able to make informed decisions on how better to control and optimise the target sites, which is where this research project has seen further opportunities. This enhanced telemetry and SCADA infrastructure will enable successful research to further develop an intelligent integrated system that tackles pump scheduling and process control with the emphasis on energy management. The use of modern techniques, such as artificial intelligence, to optimise the network operation is gradually gaining traction. The balance between implementing new technology (with the benefits it may bring) and reluctance to change from the incumbent operating model will always provide challenges in the technology adoption agenda. The main work of this research project included the physical surveying of a wastewater hydraulic catchment, inclusive of all wet well dimensions, lidar overlays, and pump electrical power characteristics. These survey results where then able to be programmed by the research into the company’s' hydraulic model to enable a higher degree of accuracy in the modelling, as well as enabling electrical power as a measurable output. From here, the model was then able to be optimised, focussing on electrical energy as an output variable for reduction. The research concluded that electrical energy consumption over time can be reduced using the aforementioned strategies and as such recommends further work to move from the model environment to physical architecture. It does so with the key message that risk tolerances on water levels must be pre-agreed with hydraulic specialists prior to deployment

A Model to support the decision process for migration to cloud computing.

Cloud computing is an emerging paradigm for provisioning computing and IT services. Migration from traditional systems setting up to cloud computing is a strategic organisational decision that can affect organisations’ performance, productivity, and growth as well as competitiveness. Organisations wishing to migrate their legacy systems to the cloud often need to go through a difficult and complicated decision-making process. This can be due to multiple factors including restructuring IT resources, the still evolving nature of the cloud environment, and the continuous expansion of the cloud services, configurations and providers. This research explores the factors that would influence decision making for migration to the cloud, its impact on IT management, and the main tasks that organisations should consider to ensure successful migration projects. The sequential exploratory strategy is followed for the exploration. This strategy is implemented through the utilisation of a two-stage survey for collecting the primary data. The analysis of the two-stage survey as well as the literature identified eleven determinants that increase the complexity in the decisions to migrate to the cloud. In the literature some of those determinants were realised, accordingly, there have been many proposed methods for supporting migration to the cloud. However, no systematic decision making process exists that clearly identifies the main steps and explicitly describes the tasks to be performed within each step. This research aims to fill this need by proposing a model to support the decision process for migrating to cloud. The model provides a structure which covers the whole process of migration decisions. It guides decision makers through a step-by-step approach aiding organisations with their decision making. The model was evaluated by exploring the views of a group of the cloud practitioners on it. The analysis of the views demonstrated a high level of acceptance by the practitioners with regard to the structure, tasks, and issues addressed by the model. The model offers an encouraging preliminary structure for developing a cloud Knowledge-Based Decision Support System

A Contingency Approach for Supply Chain Preparedness to Pursue Circular Economy Business Models

A growing stream in circular economy (CE) research is about circular economy business models (CEBM). It suggests how firms could learn to adopt unique material and product designs, newer business models, value chain networks and potential enablers that satisfies CE ideologies about economic, environment, and society. However, the understanding about how firms could integrate CEBM practices at internal, supply chain, and external levels is limited. Given the rising complexities in supply chains, the goal of this dissertation is to: (a) understand the landscape of CE concepts within the supply chain management context, and consequently (b) comprehend how firms’ preparedness about their internal, end-to-end supply chains and external environment, help them in pursuing business models that are guided by CE principles. In this dissertation, the first study provides an inclusive understanding of CE in a supply chain management context using bibliometric-network analysis. One key insight suggests CEBM is a promising theme within CE but remains unexplored in supply chain context. Using contingency theory lens, the second study identifies factors related to a focal firm’s CEBM practice as the response, its contingencies as context, its supply chain preparedness as output, and its CEBM performance as a consequent outcome. Using multi-industry multi-tier supply chain case-study method, the study explores how supply chain preparedness is related to CEBM practices and CEBM performance, and the factors upon which this relationship is contingent. A set of propositions and a contingency research framework is proposed. The research implications shall benefit scholars of transdisciplinary interests and serve as a guiding tool for practitioners and consultants presently acting upon CEBM implementation in their supply chain systems

Dynamic collaboration and secure access of services in multi-cloud environments

The cloud computing services have gained popularity in both public and enterprise domains and they process a large amount of user data with varying privacy levels. The increasing demand for cloud services including storage and computation requires new functional elements and provisioning schemes to meet user requirements. Multi-clouds can optimise the user requirements by allowing them to choose best services from a large number of services offered by various cloud providers as they are massively scalable, can be dynamically configured, and delivered on demand with large-scale infrastructure resources. A major concern related to multi-cloud adoption is the lack of models for them and their associated security issues which become more unpredictable in a multi-cloud environment. Moreover, in order to trust the services in a foreign cloud users depend on their assurances given by the cloud provider but cloud providers give very limited evidence or accountability to users which offers them the ability to hide some behaviour of the service. In this thesis, we propose a model for multi-cloud collaboration that can securely establish dynamic collaboration between heterogeneous clouds using the cloud on-demand model in a secure way. Initially, threat modelling for cloud services has been done that leads to the identification of various threats to service interfaces along with the possible attackers and the mechanisms to exploit those threats. Based on these threats the cloud provider can apply suitable mechanisms to protect services and user data from these threats. In the next phase, we present a lightweight and novel authentication mechanism which provides a single sign-on (SSO) to users for authentication at runtime between multi-clouds before granting them service access and it is formally verified. Next, we provide a service scheduling mechanism to select the best services from multiple cloud providers that closely match user quality of service requirements (QoS). The scheduling mechanism achieves high accuracy by providing distance correlation weighting mechanism among a large number of services QoS parameters. In the next stage, novel service level agreement (SLA) management mechanisms are proposed to ensure secure service execution in the foreign cloud. The usage of SLA mechanisms ensures that user QoS parameters including the functional (CPU, RAM, memory etc.) and non-functional requirements (bandwidth, latency, availability, reliability etc.) of users for a particular service are negotiated before secure collaboration between multi-clouds is setup. The multi-cloud handling user requests will be responsible to enforce mechanisms that fulfil the QoS requirements agreed in the SLA. While the monitoring phase in SLA involves monitoring the service execution in the foreign cloud to check its compliance with the SLA and report it back to the user. Finally, we present the use cases of applying the proposed model in scenarios such as Internet of Things (IoT) and E-Healthcare in multi-clouds. Moreover, the designed protocols are empirically implemented on two different clouds including OpenStack and Amazon AWS. Experiments indicate that the proposed model is scalable, authentication protocols result only in a limited overhead compared to standard authentication protocols, service scheduling achieves high efficiency and any SLA violations by a cloud provider can be recorded and reported back to the user.My research for first 3 years of PhD was funded by the College of Engineering and Technology

Application of systems thinking in reviewing power-infrastructure capital investment in South Africa.

Doctoral Degree. University of KwaZulu-Natal, Durban.The aim of this study was to investigate how the application of systems thinking can minimize revenue losses in capital power-infrastructure investment in South Africa. The study reviewed the application of existing financial models such as Return on Investments (ROI), Internal Rate of Return (IRR), and Schedule Performance Index (SPI) to multi-years of capital power-infrastructure investments with an intention of introducing new investment evaluation model. In order to achieve the study objective, the researcher had to investigate using the systems approach the challenges regarding development of capital power-infrastructure investments, and apply systems thinking in evaluating the positive impact of timeous payments by debtors. This study was conducted mainly within Eskom comprising 150 engineers that are in the capital power-infrastructure process. Seven of the engineers who are in executive position participated in the qualitative part of the study and 90 engineers participated in the quantitative part of the study. In other words, the sample of the study comprised of 97 engineers involved in capital power-infrastructure investment. The research employed both quantitative and qualitative mixed method approach. The study found that knowledge management and corporate governance in power utilities of South Africa, including Eskom, is very weak. Furthermore, financial models used such as Internal Rate of Return, Return on Investments, Net Present Value, Level Cost of Energy and Cost of Unserved Energy did not realize the envisaged benefits. Other problems identified by the study included but not limited to multi-packages of contracting instead of single contracting for turnkey solution, lack of understanding the environmental history of where infrastructure was to be constructed, complication caused by procuring services from foreign companies, and lack of proper front- end planning. The participants that were subject matter experts in capital power-infrastructure investments linear regression analysis have proven the consistent relationship between scope liquidity and cost variances and further concurred that current financial models used to assess returns are mostly not realized. The study recommends the formation of a special governance committee that will ensure that there are front-end planning processes, including the application Complexity Factor, as the way of ensuring financial returns and enable successful delivery of capital power-infrastructure investment. The proposed committee should also advise on the suitability of the service provider in provision of the turnkey solution. The recommended special governance committee should also ensure competency of foreign companies in alignment with Supply Chain Management requirements of South Africa during bid evaluation and adjudicating. The study further recommends a systems model that can be used by power utilities to ensure that initial envisaged benefits are realized

Institutional plan. Fiscal year, 1997--2002

The Institutional Plan is the culmination of Argonne`s annual planning cycle. The document outlines what Argonne National Laboratory (ANL) regards as the optimal development of programs and resources in the context of national research and development needs, the missions of the Department of Energy and Argonne National Laboratory, and pertinent resource constraints. It is the product of ANL`s internal planning process and extensive discussions with DOE managers. Strategic planning is important for all of Argonne`s programs, and coordination of planning for the entire institution is crucial. This Institutional Plan will increasingly reflect the planning initiatives that have recently been implemented

Contextualising energy justice in low-income built environment: Towards data-driven policy interventions for addressing distributive injustices in slum rehabilitation housing of the Global South

Around a billion people live in slums today globally, and rehabilitating them to formal housing is a significant challenge. Slum rehabilitation housing is a policy effort to solve this crisis and alleviate urban poverty. However, the question of whether slum rehabilitation programmes are accomplishing more good than harm or whether they are creating a whole host of new problems remains unexplored in the literature. This thesis investigates the effect of slum rehabilitation on household energy demand in Brazil, India and Nigeria through the lens of distributive energy justice. Furthermore, this thesis makes methodological innovation to aid in just policy design by improving the objectivity of including local and contextual knowledge on how poor households live and use energy. Doing so makes novel theoretical and methodological contributions: a theoretical contribution to temporality and spatial energy justice studies on how to offer cross-sectional depictions of energy demand within the slum rehabilitation housing, which was evaluated through structural equation modelling, and a methodological contribution in developing a deep-narrative analysis framework using natural language processing and machine learning-based Latent Dirichlet Allocation algorithm to capture the grounded narratives of distributive injustices objectively. This research highlighted the significance of contextualisation in planning for energy justice in slum communities and the role of digital tools like natural language processing in objectively integrating grounded narratives in just policy design. The contextualisation was done through zoom-in and zoom-out of the grounded narratives enabled through the multi-method approach. Zooming-out view of distributed injustices in the study areas of Mumbai (India), Rio de Janeiro (Brazil) and Abuja (Nigeria) revealed inefficiencies in the administration of electricity distribution companies, lumped billing periods and lack of people-centric built environment design considerations. Similarly, zooming-in the case studies revealed that the poor design of the slum rehabilitation-built environment influenced the increase in energy intensity in the Mumbai case, leading to energy poverty. Whereas created distinct poverty traps in the Brazilian and Nigerian cases through frequent power cuts, high cost of appliance repair, and poor housing design. Finally, policy implications were drawn as per the policy actors across municipal, state and national levels that suggested leveraging digital tools like the deep-narrative analysis and the heavy penetration of Information and Communication Technology devices in such low-income communities. Such tools can improve accountability in decision-making and improve the representation of the occupants through their narratives of injustices associated with living in such communities. Thus, this thesis uniquely forwarded a data-driven pathway for integrating local collective intelligence in just policy design.Bill and Melinda Gates Foundation through the Gates Cambridge Scholarship under the Grant Number OPP1144

Leveraging Public Resource Pools to Improve the Service Compliances of Computing Utilities

Abstract. Computing utilities are emerging as an important part of the infrastructure for outsourcing computer services. One of the major objectives of computing utilities is to maximize their net profit while maintaining customer loyalty in accordance with the service level agreements (SLAs). Defining the SLAs conservatively might be one easy way to achieve SLA compliance, but this results in underutilization of resources and loss of revenue in turn. In this paper, we show that inducting unreliable public resources into a computing utility enables more competetive SLAs while maintaining higher level of runtime compliance as well as maximizing profit.