July 2012 Archives

Evangelos Rikos

NTUA researchers presentation at Athens, Greece, 20th July 2012.

Flexibility objects, objects with flexibilities in time and amount dimensions (e.g., energy or product amount), occur in many scientic and commercial domains. Managing such objects with existing DBMSs is infeasible due to the complexity, data volume, and complex functionality needed, so a new kind of flexibility database is needed. This paper is the first to consider flexibility databases. It formally defines the concept of flexibility objects (flex-objects), and provide a novel and efficient solution for aggregating and disaggregating flex-objects. This is important for a range of applications, including smart grid energy management. The paper considers the grouping of flex-objects, alternatives for computing aggregates, the disaggregation process, their associated requirements, as well as efficient incremental computation. Extensive experiments based on data from a real-world energy domain project show that the proposed solution provides good performance while still satisfying the strict requirements.

Forecasting is an important analysis technique used in many application domains such as electricity management, sales and retail and, traffic predictions. The employed statistical models already provide very accurate predictions, but recent developments in these domains pose new requirements on the calculation speed of the forecast models. Especially, the often used multi-equation models tend to be very complex and their estimation is very time consuming. To still allow the use of these highly accurate forecast models, it is necessary to improve the data processing capabilities of the involved data management systems. For this purpose, we introduce a partitioning approach for multi-equation forecast models that considers the specific data access pattern of these models to optimize the data storage and memory access. With the help of our approach we avoid the redundant reading of unnecessary values and improve the utilization of the CPU cache. Furthermore, we utilize the capabilities of modern multi-core hardware and parallelize the model estimation. Our experimental results on real-world data show speedups of up to 73x for the initial model estimation. Thus, our partitioning and parallelization approach significantly increases the efficiency of multi-equation models.

Integrating sophisticated statistical methods into database management systems is gaining more and more attention in research and industry. One important statistical method is time series forecasting, which is crucial for decision management in many domains. In this context, previous work addressed the processing of ad-hoc and recurring forecast queries. In contrast, we focus on subscription-based forecast queries that arise when an application (subscriber) continuously requires forecast values for further processing. Forecast queries exhibit the unique characteristic that the underlying forecast model is updated with each new actual value and better forecast values might be available. However, (re-)sending new forecast values to the subscriber for every new value is infeasible because this can cause signicant overhead at the subscriber side. The subscriber therefore wishes to be notied only when forecast values have changed relevant to the application. In this paper, we reduce the costs of the subscriber by optimizing the notications sent to the subscriber, i.e., by balancing the number of notifications and the notification length. We introduce a generic cost model to capture arbitrary subscriber cost functions and discuss dierent optimization approaches that reduce the subscriber costs while ensuring constrained forecast values deviations. Our experimental evaluation on real datasets shows the validity of our approach with low computational costs.

In the MIRABEL project, a data management system for a smart grid is developed to enable smarter scheduling of energy consumption such that, e.g., charging of car batteries is done during night when there is an overcapacity of green energy from windmills etc. Energy can then be requested by means of flexoffers which define flexibility with respect to time, amount, and/or price. In this paper, we describe MIRABEL DW, a data warehouse (DW) for the management of the large amounts of complex energy data in MIRABEL. We present a unified schema that can manage data both at the level of the entire electricity network and at the level of individual nodes, such as a single consumer node. The schema has a number of complexities compared to typical DW schemas. These include facts about facts and composed non-atomic facts and unified handling of different kinds of flex-offers and time series. We also discuss alternative data modeling strategies and present typical queries from the energy domain and a performance study.

Jack Verhoosel, Diederik Rothengatter, Frens-Jan Rumph and Mente Konsman. In: 3rd Workshop on eeBuildings Data Models (Energy Efficiency Vocabularies), July 2012.

To address the needs of rapidly changing energy markets, an energy data management system capable of supporting higher utilization of renewable energy sources is being developed. The system receives flexible offers from producers and consumers of energy, aggregates them on a regional level and schedules the aggregated flexible offers to balance forecast energy supply and demand. This paper focuses on formulating and solving the optimization problem of scheduling aggregated flexible offers within such a system. Three metaheuristic scheduling algorithms (a randomized greedy search, an evolutionary algorithm and a hybrid between the two) tailored to this problem are introduced and their performance is assessed on a benchmark test problem and two realistic problems. The best results are achieved by the evolutionary algorithms, which can efficiently handle thousands of aggregated flex-offers.

Changing electricity markets call for new ways of handling supply and demand. The desired goal is to increase the utilization of renewable energy while ensuring reliable supply and minimizing the costs. We present an approach aiming at this goal that handles a large number of flexible energy offers from producers and consumers by aggregating them and scheduling these aggregates to minimize a cost function. We explore the influence of aggregation on the performance of scheduling, establishing that a trade-off between keeping the flexibilities of flexible offers and reducing their number is what yields the best results.