An Automatic Aggregator of Power Flexibility in Smart Buildings Using Software Based Orchestration

This paper presents a software-based modular and hierarchical building energy management system (BEMS) to control the power consumption in sensor-equipped buildings. In addition, the need of this type of solution is also highlighted by presenting the worldwide trends of thermal energy end use in buildings and peak power problems. Buildings are critical component of smart grid environments and bottom-up BEMS solutions are need of the hour to optimize the consumption and to provide consumption side flexibility. This system is able to aggregate the controls of the all-controllable resources in building to realize its flexible power capacity. This system provides a solution for consumer to aggregate the controls of ‘behind-the-meter’ small loads in short response and provide ‘deep’ demand-side flexibility. This system is capable of discovery, status check, control and management of networked loads. The main novelty of this solution is that it can handle the heterogeneity of the installed hardware system along with time bound changes in the load device network and its scalability; resulting in low maintenance requirements after deployment. The control execution latency (including data logging) of this BEMS system for an external control signal is less than one second per connected load. In addition, the system is capable of overriding the external control signal in order to maintain consumer coziness within the comfort temperature thresholds. This system provides a way forward in future for the estimation of the energy stored in the buildings in the form of heat/temperature and use buildings as temporary batteries when electricity supply is constrained or abundant

Keyword-based search in peer-to-peer networks

Ph.DDOCTOR OF PHILOSOPH

A Paradigm for Scalable, Transactional, and Efficient Spatial Indexes

With large volumes of geo-tagged data collected in various applications, spatial query pro- cessing becomes essential. Query engines depend on efficient indexes to expedite processing. There are three main challenges: scaling out to accommodate large volumes of spatial data, support- ing transactional primitives for strong consistency guarantees, and adapting to highly dynamic workloads. This thesis proposes a paradigm for scalable, transactional, and efficient spatial indexes to significantly reduce development efforts in designing and comparing multiple spatial indexes.This thesis first introduces a distributed and transactional key value store called DTranx to persist the spatial indexes. DTranx follows the SEDA architecture to exploit high concurrency in multi-core environments and it adopts a hybrid of optimistic concurrency control and two-phase commit protocols to narrow down the critical sections of distributed locking during transaction com- mits. Moreover, DTranx integrates a persistent memory based write-ahead log to reduce durability overhead and combines a garbage collection mechanism without affecting normal transactions. To maintain high throughput for search workloads when databases are constantly updated, snapshot transactions are introduced.Then, a paradigm is presented with a set of intuitive APIs and a Mempool runtime to re- duce development efforts. Mempool transparently synchronizes local states of data structures with DTranx and it handles two critical tasks: address translation and transparent server synchroniza- tion, of which the latter includes transaction construction and data synchronization. Furthermore, a dynamic partitioning strategy is integrated into DTranx to generate partitioning and replication plans that reduce inter-server communications and balance resource usage.Lastly, single-threaded data structures BTree and RTree are converted into distributed versions within two weeks. The BTree and RTree achieve 253.07 kops/sec and 77.83 kops/sec through- put respectively for pure search operations in a 25-server cluster

Secured Data Masking Framework and Technique for Preserving Privacy in a Business Intelligence Analytics Platform

The main concept behind business intelligence (BI) is how to use integrated data across different business systems within an enterprise to make strategic decisions. It is difficult to map internal and external BI’s users to subsets of the enterprise’s data warehouse (DW), resulting that protecting the privacy of this data while maintaining its utility is a challenging task. Today, such DW systems constitute one of the most serious privacy breach threats that an enterprise might face when many internal users of different security levels have access to BI components. This thesis proposes a data masking framework (iMaskU: Identify, Map, Apply, Sign, Keep testing, Utilize) for a BI platform to protect the data at rest, preserve the data format, and maintain the data utility on-the-fly querying level. A new reversible data masking technique (COntent BAsed Data masking - COBAD) is developed as an implementation of iMaskU. The masking algorithm in COBAD is based on the statistical content of the extracted dataset, so that, the masked data cannot be linked with specific individuals or be re-identified by any means. The strength of the re-identification risk factor for the COBAD technique has been computed using a supercomputer where, three security scheme/attacking methods are considered, a) the brute force attack, needs, on average, 55 years to crack the key of each record; b) the dictionary attack, needs 231 days to crack the same key for the entire extracted dataset (containing 50,000 records), c) a data linkage attack, the re-identification risk is very low when the common linked attributes are used. The performance validation of COBAD masking technique has been conducted. A database schema of 1GB is used in TPC-H decision support benchmark. The performance evaluation for the execution time of the selected TPC-H queries presented that the COBAD speed results are much better than AES128 and 3DES encryption. Theoretical and experimental results show that the proposed solution provides a reasonable trade-off between data security and the utility of re-identified data

Low carbon multi-vector energy systems: a case study of the University of Edinburgh's 2040 'Net Zero' target

The ultimate goal of this research was to develop a methodology to support decision-making by large (public sector) organisations regarding future energy technology choices to reduce carbon emissions. This culminated in the development of a multi-vector campus energy systems modelling tool that was applied to the University of Edinburgh as a case study. To deliver this a series of objectives were addressed. Machine learning models were applied to model building heat and electrical energy use for extrapolation to campus level. This was applied to explore the scope to reduce campus level emissions through operational changes; this demonstrated that it is difficult to further reduce the carbon emissions without technological changes given the University’s heavy reliance on natural gas-fired combined heat and power and boilers. As part of the analysis of alternative energy sources, the scope for off-campus wind farms was considered; specifically this focussed on estimation of wind farm generation at the planning stage and employed a model transfer strategy to facilitate use of metered data from wind farms. One of the key issues in making decisions about future energy sources on campus is the simultaneous changes in the wider energy system and specifically the decarbonisation of electricity; to facilitate better choices about onsite production and imports from the grid, a fundamental electricity model was developed to translate the National Grid Future Energy Scenarios into plausible patterns of electricity prices. The learning from these activities were incorporated into a model able to develop possible configurations for campus-level multi-vector energy systems given a variety of future pathways and uncertainties. The optimal planning model is formulated as a mixed-integer linear programming model with the objective to minimize the overall cost including carbon emissions. A numerical case study for the planning of three real-world campuses is presented to demonstrate the effectiveness of the proposed method. The conclusion highlights the importance of energy storage and a remote wind farm in these energy systems. Also, it is noted that there is no single solution that works in all cases where there are differences in factors such as device cost and performance, the gap between gas and electricity prices, weather conditions and the use (or otherwise) of cross-campus local energy balancing

High performance systems

This document provides a written compilation of the presentations and viewgraphs from the 1994 Conference on High Speed Computing given at the High Speed Computing Conference, {open_quotes}High Performance Systems,{close_quotes} held at Gleneden Beach, Oregon, on April 18 through 21, 1994