Holistic Measures for Evaluating Prediction Models in Smart Grids

The performance of prediction models is often based on "abstract metrics" that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging "big data" domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost. We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario. While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability. Our results underscore the value of the proposed measures to offer a deeper insight into models' behavior and their impact on real applications, which benefit both data mining researchers and practitioners.Comment: 14 Pages, 8 figures, Accepted and to appear in IEEE Transactions on Knowledge and Data Engineering, 2014. Authors' final version. Copyright transferred to IEE

Efficient ICT for efficient smart grids

In this extended abstract the need for efficient and reliable ICT is discussed. Efficiency of ICT not only deals with energy-efficient ICT hardware, but also deals with efficient algorithms, efficient design methods, efficient networking infrastructures, etc. Efficient and reliable ICT is a prerequisite for efficient Smart Grids. Unfortunately, efficiency and reliability have not always received the proper attention in the ICT domain in the past

Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System

A Case Study of Economic Optimization of HVAC Systems based on the Stanford University Campus Airside and Waterside Systems

Commercial buildings account for $200 billion per year in energy expenditures, with heating, ventilation, and air conditioning (HVAC) systems accounting for most of these costs. In energy markets with time-varying prices and peak demand charges, a significant potential for cost savings is provided by using thermal energy storage to shift energy loads. Since most implementations of HVAC control systems do not optimize energy costs, they have become a primary focus for new strategies aimed at economic optimization. Model predictive control (MPC) has emerged as one popular method to achieve this load shifting, while respecting system constraints. MPC uses a model of the system to make predictions and to solve an optimization problem. Much research has shown the benefits of MPC over alternative strategies for HVAC control [1]. However, some industrial applications, such as large research centers or university campuses, are too large to be solved in a single MPC instance. Decompositions have been proposed in the literature, but it is difficult to evaluate and to compare decompositions against one another when using different systems. In this paper, we present a large-scale relevant case study where solving a single MPC optimization problem is neither desirable nor feasible for real-time implementations. The study is modeled after the Stanford University campus, consisting of both an airside and waterside system [2]. The airside system includes 500 zones spread throughout 25 campus buildings along with the air handler units and regulatory building automation system used for temperature regulation. The waterside system includes the central plant equipment, such as chillers, that is used to meet the load from the buildings. Active thermal energy storage is available to the campus in addition to the passive thermal energy storage present in the form of building mass. The airside models describe the temperature dynamics in each of the 500 zones, and the waterside models describe the power consumption of the central plant equipment. The aim of the control system is to minimize costs in the presence of time-varying electricity prices and a peak demand charge as well as environmental disturbances such as weather while meeting constraints on comfort and equipment. We perform an economic optimization of the entire campus using a hierarchical system with distributed airside controllers to demonstrate the potential savings. The models from this case study are made publicly available for other researchers interested in designing alternative control strategies for managing chilled water production to meet airside loads. The aim of the case study release is to provide a standardized problem for the research community. A benchmark is provided for evaluating performance. References [1] A. Afram and F. Janabi-Sharifi. Theory and applications of HVAC control systems—A review of model predictive control (MPC). Building and Environment, 72:343–355, February 2014. [2] J. B. Rawlings, N. R. Patel, M. J. Risbeck, C. T. Maravelias, M. J. Wenzel, and R. D. Turney. Economic MPC and real-time decision making with application to large-scale HVAC energy systems. Computers & Chemical Engineering, 2017. In Press

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems