Qunzhi Zhou

Cloud-Based Software Platform for Big Data Analytics in Smart Grids

This article focuses on a scalable software platform for the Smart Grid cyber-physical system using cloud technologies. Dynamic Demand Response (D2R) is a challenge-application to perform intelligent demand-side management and relieve peak load in Smart Power Grids. The platform offers an adaptive information integration pipeline for ingesting dynamic data; a secure repository for researchers to share knowledge; scalable machine-learning models trained over massive datasets for agile demand forecasting; and a portal for visualizing consumption patterns, and validated at the University of Southern Californias campus microgrid. The article examines the role of clouds and their tradeoffs for use in the Smart Grid Cyber-Physical Sagileystem.

Semantic Information Modeling for Emerging Applications in Smart Grid

Smart Grid modernizes power grid by integrating digital and information technologies. Millions of smart meters, intelligent appliances and communication infrastructures are under deployment allowing advanced IT applications to be developed to secure and manage power grid operations. Demand response (DR) is one such emerging application to optimize electricity demand by curtailing/shifting power load when peak load occurs. Existing DR approaches are mostly based on static plans such as pricing policies and load shedding schedules. However, improvements to power management applications rely on data emanating from existing and new information sources with the growth of Smart Grid information space. In particular, dynamic DR algorithms depend on information from smart meters that report interval-based power consumption measurement, HVAC systems that monitor buildings heat and humidity, and even weather forecast services. In order for emerging Smart Grid applications to take advantage of the diverse data influx, extensible information integration is required. In this paper, we develop an integrated Smart Grid information model using Semantic Web techniques and present case studies of using semantic information for dynamic DR. We show the semantic model facilitates information integration and knowledge representation for developing the next generation Smart Grid applications.

Semantic Information Integration for Smart Grid Applications

The Los Angeles Smart Grid Project aims to use informatics techniques to bring about a quantum leap in the way demand response load optimization is performed in utilities. Semantic information integration, from sources as diverse as Internet-connected smart meters and social networks, is a linchpin to support the advanced analytics and mining algorithms required for this. In association with it, semantic complex event processing system will allow consumer and utility managers to easily specify and enact energy policies continuously. We present the information systems architecture for the project that is under development, and discuss research issues that emerge from having to design a system that supports 1.4 million customers and a rich ecosystem of Smart Grid applications from users, third party vendors, the utility and regulators.

Toward data-driven demand-response optimization in a campus microgrid

We describe and demonstrate a prototype software architecture to support data-driven demand response optimization (DR) in the USC campus microgrid, as part of the Los Angeles Smart Grid Demonstration Project. The architecture includes a semantic information repository that integrates diverse data sources to support DR, demand forecasting using scalable machine-learned models, and detection of load curtailment opportunities by matching complex event patterns.

Towards an inexact semantic complex event processing framework

Complex event processing (CEP) deals with detecting real-time situations, represented as event patterns, from among an event cloud. The state-of-the-art CEP systems process events as plain data tuples and are limited to detect precisely defined patterns. Emerging application areas like optimization in smart power grids require CEP to incorporate semantic knowledge of the domain for easier pattern specification, and detect inexact patterns in the presence of uncertainties. In this paper, we present motivating use cases, discuss limitations of existing CEP systems and describe our work towards an Inexact Semantic Complex Event Processing (InSCEP) framework.

Towards hybrid online on-demand querying of realtime data with stateful complex event processing

Emerging Big Data applications in areas like ecommerce and energy industry require both online and on-demand queries to be performed over vast and fast data arriving as streams. These present novel challenges to Big Data management systems. Complex Event Processing (CEP) is recognized as a high performance online query scheme which in particular deals with the velocity aspect of the 3-Vs of Big Data. However, traditional CEP systems do not consider data variety and lack the capability to embed ad hoc queries over the volume of data streams. In this paper, we propose H2O, a stateful complex event processing framework, to support hybrid online and on-demand queries over realtime data. We propose a semantically enriched event and query model to address data variety. A formal query algebra is developed to precisely capture the stateful and containment semantics of online and on-demand queries. We describe techniques to achieve the interactive query processing over realtime data featured by efficient online querying, dynamic stream data persistence and on-demand access. The system architecture is presented and the current implementation status reported.

Incorporating semantic knowledge into dynamic data processing for smart power grids

Semantic Web allows us to model and query time-invariant or slowly evolving knowledge using ontologies. Emerging applications in Cyber Physical Systems such as Smart Power Grids that require continuous information monitoring and integration present novel opportunities and challenges for Semantic Web technologies. Semantic Web is promising to model diverse Smart Grid domain knowledge for enhanced situation awareness and response by multi-disciplinary participants. However, current technology does pose a performance overhead for dynamic analysis of sensor measurements. In this paper, we combine semantic web and complex event processing for stream based semantic querying. We illustrate its adoption in the USC Campus Micro-Grid for detecting and enacting dynamic response strategies to peak power situations by diverse user roles. We also describe the semantic ontology and event query model that supports this. Further, we introduce and evaluate caching techniques to improve the response time for semantic event queries to meet our application needs and enable sustainable energy management.

A Semantic Framework for Integrated Modeling and Simulation of Transportation Systems

Abstract A number of modeling and simulation tools exist for studying transportation systems. The value of these tools will be significantly increased if they can be used in an integrated manner to investigate scenarios involving domain aspects modeled by different tools. This paper describes the design of an integrated traffic modeling and simulation framework using Semantic Web technology. The core component of the system is a domain ontology model which captures domain concepts and forms the common vocabulary for data and application integration. The proposed framework is designed to be modular and extensible, and to accommodate advanced visualization and human interaction together with data access and management capabilities. It hides the disparity of data formats, models and tools from domain experts and provides a single logical space for simulation design. We describe the technologies to achieve these goals and discuss the methodologies through representative use cases in studying transportation systems and control algorithms.

Knowledge-infused and consistent Complex Event Processing over real-time and persistent streams

Emerging applications in Internet of Things (IoT) and CyberPhysical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. In particular, we also address temporal consistency issues that arise during fault recovery of query plans that span the boundary between real-time and persistent streams. The proposed -CEPquery model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment. Our results show that we are able to sustain a processing throughput of 3,000 events/secs for -CEPqueries, a 30 improvement over the baseline and sufficient to support a Smart Township, and can resume consistent processing within 20 secs after stream outages as long as 2hours. A semantic CEP model is introduced to query across real-time and persistent streams.The models analytic capability is illustrated using case studies from Smart Grid.Approaches to translate the model into scalable execution are discussed and evaluated.

Towards an integrated modeling and simulation framework for freight transportation in metropolitan areas

Freight transportation at distribution nodes such as marine ports, airports and rail yards has been putting tremendous environmental pressure in metropolitan areas. A prerequisite for proposing any solution that would make the existing systems more efficient is an accurate analysis and understanding of freight movements. A single model cannot fully capture aspects of freight transportation which interact and affect each other in a complex manner. Rather, integration of a variety of legacy simulation and analysis tools along with holistic optimization is a necessity for freight transportation system design. This paper proposes an integrated modeling and simulation framework for freight transportation using semantic web technology which offers benefits of modularity, extensibility and reusability of both code and design to the applications. We discuss the implementation strategies and methods to achieve these goals and identify some of the key research challenges in realizing our framework vision.