Recently in Publications Category

We demonstrate TimeTravel, an efficient DBMS system for seamless integrated querying of past and (forecasted) future values of time series, allowing the user to view past and future values as one joint time series. This functionality is important for advanced application domain like energy. The main idea is to compactly represent time series as models. By using models, the TimeTravel system answers queries approximately on past and future data with error guarantees (absolute error and confidence) one order of magnitude faster than when accessing the time series directly. In addition, it efficiently supports exact historical queries by only accessing relevant portions of the time series. This is unlike existing approaches, which access the entire time series to exactly answer the query.
To realize this system, we propose a novel hierarchical model index structure. As real-world time series usually exhibits seasonal behavior, models in this index incorporate seasonality. To construct a hierarchical model index, the user specifies seasonality period, error guarantees levels, and a statistical forecast method. As time proceeds, the system incrementally updates the index and utilizes it to answer approximate and exact queries. TimeTravel is implemented into PostgreSQL, thus achieving complete user transparency at the query level. In the demo, we show the easy building of a hierarchical model index for a real-world time series and the effect of varying the error guarantees on the speed up of approximate and exact queries.

Integrating sophisticated statistical methods into database management systems is gaining more and more attention in research and industry in order to be able to cope with increasing data volume and increasing complexity of the analytical algorithms. One important statistical method is time series forecasting, which is crucial for decision making processes in many domains. The deep integration of time series forecasting offers additional advanced functionalities within a DBMS. More importantly, however, it allows for optimizations that improve the efficiency, consistency, and transparency of the overall forecasting process. To enable efficient integrated forecasting, we propose to enhance the traditional 3-layer ANSI/SPARC architecture of a DBMS with forecasting functionalities. This article gives a general overview of our proposed enhancements and presents how forecast queries can be processed using an example from the energy data management domain. We conclude with open research topics and challenges that arise in this area.

Nowadays, Renewable Energy Sources (RES) are attracting more and more interest. Thus, many countries aim to increase the share of green energy and have to face with several challenges (e.g., balancing, storage, pricing). In this paper, we address the balancing challenge and present the MIRABEL project which aims to prototype an Energy Data Management System (EDMS) which takes benefit of flexibilities to efficiently balance energy demand and supply. The EDMS consists of millions of heterogeneous nodes that each incorporates advanced components (e.g., aggregation, forecasting, scheduling, negotiation). We describe each of these components and their interaction. Preliminary experimental results confirm the feasibility of our EDMS.

The contribution describes the demand response solution, which was developed within the international project Mirabel and cofounded by the European 7th Framework Program. The main goal of the project is to provide a solution, which controls the peak consumption and provide a more efficient integration of the renewable sources into the electricity grid. The uniqueness of the solution is the economic concept, where the consumer makes a closed energy supply contract for that part of its consumption, which is put under the control of the external party - Balance responsible party. That approach is in relation of the energy transfer on the organized market, which is close to the BRP's energy handling practice. The algorithms of the data aggregation, consumption and production forecasting and energy flow scheduling developed within the project enable the integration of the large amount of consumers into the Mirabel solution. The preliminary results presented are generated during the testing phase of the project, which is being executed for the time of the generating this paper. The testing is being executed in the simulation environment and with the real data provided by the utility project partner.

The trial cases used for the testing of the Mirabel product follows the strategic impact defined in the Description of Work. It is expected that within the mass of the electricity consumers the Mirabel solution will enable increase the integrated share of RES in EES by 5% and reduce the peak demand for at least 5%, with a targeted approximate 8-9% for the total grid.
The delivery describes and resolves the issues necessary to execute the testing and provide the laboratory evaluation of the Mirabel solution. That includes description of the scenarios for the trial cases, definition of the testing environment and the definition of the successfulness criteria.

Use of renewable energy sources is enforced by national and international regulations.
Drivers for such policies include mitigation of climate change due to emission of
greenhouse gasses and reducing dependency on fossil fuel reserves. Due to the
intermittent character of renewable energy sources such as photovoltaic or wind power,
integration of such sources creates a challenge in maintaining balance between demand
and supply. Indications of such challenges in countries with e.g. a high penetration of
wind power are already showing in prices on power exchanges reaching zero or negative
energy prices. In general, without mitigation measures, an increase in the use of
intermittent renewable energy sources leads to a diminished ability to guarantee security
of supply.

The aim of the MIRABEL project is to exploit potential flexibility in both demand for and
generation of electricity to compensate for the intermittent nature of Renewable Energy
Sources (RES). For instance by postponing certain electricity demand until energy from a
RES (e.g. solar panel) becomes available.

In order to leverage flexibility in demand and generation means for expressing and
communicating this flexibility are needed. For this purpose, this MIRABEL deliverable
provides a data model. This document contains the final version of the data model, a
previous version of the data model was presented in deliverable D2.2 which was
published in December 2010.

Forecasting is an important analysis technique to support decisions and functionalities in many application domains. While the employed statistical models often provide a sufficient accuracy, recent developments pose new challenges to the forecasting process. Typically the available time for estimating the forecast models and providing accurate predictions is significantly decreasing. This is especially an issue in the energy domain, where forecast models often consider external influences to provide a high accuracy. As a result, these models exhibit a higher number of parameters, resulting in increased estimation efforts. Also, in the energy domain new measurements are constantly appended to the time series, requiring a continuous adaptation of the models to new developments. This typically involves a parameter re-estimation, which is often almost as expensive as the initial estimation, conflicting with the requirement for fast forecast computation. To address these challenges, we present a framework that allows a more efficient integration of external information. First, external information are handled in a separate model, because their linear and non-linear relationships are more stable and thus, they can be excluded from most forecast model adaptations. Second, we directly optimize the separate model using feature selection and dimension reduction techniques. Our evaluation shows that our approach allows an efficient integration of external information and thus, an increased forecasting accuracy, while reducing the re-estimation efforts.

The so-called smart grid is emerging in the energy domain as a solution to provide a stable, efficient and sustainable energy supply accommodating ever growing amounts of renewable energy like wind and solar in the energy production. Smart grid systems are highly distributed, manage large amounts of energy related data, and must be able to react rapidly (but intelligently) when conditions change, leading to substantial real-time business intelligence challenges. This paper discusses these challenges and presents data management solutions in the European smart grid project \Mirabel. These solutions include real-time time series forecasting, real-time aggregation of the flexibilities in energy supply and demand, managing subscriptions for forecasted and flexibility data, efficient storage of time series and flexibilities, and real-time analytical query processing spanning past and future (forecasted) data. Experimental studies show that the proposed solutions support important real-time business intelligence tasks in a smart grid system.

Time series forecasting is challenging as sophisticated forecast models are computationally expensive to build. Recent research has addressed the integration of forecasting inside a DBMS. One main benefit is that models can be created once and then repeatedly used to answer forecast queries. Often forecast queries are submitted on higher aggregation levels, e.g., forecasts of sales over all locations. To answer such a forecast query, we have two possibilities. First, we can aggregate all base time series (sales in Austria, sales in Belgium ...) and create only one model for the aggregate time series. Second, we can create models for all base time series and aggregate the base forecast values. The second possibility might lead to a higher accuracy but it is usually too expensive due to a high number of base time series. However, we actually do not need all base models to achieve a high accuracy, a sample of base models is enough. With this approach, we still achieve a better accuracy than an aggregate model, very similar to using all models, but we need less models to create and maintain in the database. We further improve this approach if new actual values of the base time series arrive at different points in time. With each new actual value we can refine the aggregate forecast and eventually converge towards the real actual value. Our experimental evaluation using several real-world data sets, shows a high accuracy of our approaches and a fast convergence towards the optimal value with increasing sample sizes and increasing number of actual values respectively.