Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway

The development of Smart Grid in Norway in specific and Europe/US in general will shortly lead to the availability of massive amount of fine-grained spatio-temporal consumption data from domestic households. This enables the application of data mining techniques for traditional problems in power system. Clustering customers into appropriate groups is extremely useful for operators or retailers to address each group differently through dedicated tariffs or customer-tailored services. Currently, the task is done based on demographic data collected through questionnaire, which is error-prone. In this paper, we used three different clustering techniques (together with their variants) to automatically segment electricity consumers based on their consumption patterns. We also proposed a good way to extract consumption patterns for each consumer. The grouping results were assessed using four common internal validity indexes. We found that the combination of Self Organizing Map (SOM) and k-means algorithms produce the most insightful and useful grouping. We also discovered that grouping quality cannot be measured effectively by automatic indicators, which goes against common suggestions in literature.Comment: 12 pages, 3 figure

Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods

A study on implementing probabilistic packet marking in IPv6

Lack of source authentication in the IP protocol helps to encourage denial-of-service attacks. The open and trusting nature of the protocol makes the task of identifying an attacker difficult if the attacker chooses to spoof the source address. Probabilistic Packet Marking (PPM) is an IP traceback approach that seeks to identify attackers by marking individual packets with portion of the attack path, and relies on the volume of attack traffic generated to reconstruct the whole path. In this work, we consider the fragmentation problem associated with the overloading of the identification field in IPv4 packet header in PPM implementation, and demonstrate how this can be resolved in IPv6. We show that the flow label field in the IPv6 datagram header can be safely and effectively overloaded to implement PPM schemes, and present simulation results verifying the applicability and efficiency of our approach

Performance analysis of probabilistic packet marking in IPv6

Probabilistic packet marking (PPM) has received considerable attention as an IP traceback approach against distributed Denial-of-Service attack, which is one of the most challenging security threat in the Internet. PPM is a technique that seeks to identify the source of such attacks by marking individual packets with portion of the attack path, and then relies on the volume of attack traffic generated to ensure that the whole path can be reconstructed. However, modifying the identification field in the IPv4 packet header to mark packet incurs backward incompatibility for IP fragmented packets. In this paper, we address this issue and analyze the viability of PPM under the next-generation Internet Protocol, IPv6. In doing so, we consider the flaws inherent to IPv4 implementations that limit their backward compatibility, and demonstrate how these shortcomings can be avoided in IPv6. We show that the Flow Label field in the IPv6 datagram header can be safely and effectively overloaded to implement PPM schemes, and present simulation results verifying the applicability and efficiency of this approach. © 2007 Elsevier B.V. All rights reserved

Autonomic virtual resource management for service hosting platforms

International audienceCloud platforms host several independent applications on a shared resource pool with the ability to allocate com- puting power to applications on a per-demand basis. The use of server virtualization techniques for such platforms provide great flexibility with the ability to consolidate sev- eral virtual machines on the same physical server, to resize a virtual machine capacity and to migrate virtual machine across physical servers. A key challenge for cloud providers is to automate the management of virtual servers while taking into account both high-level QoS requirements of hosted applications and resource management costs. This paper proposes an autonomic resource manager to con- trol the virtualized environment which decouples the provi- sioning of resources from the dynamic placement of virtual machines. This manager aims to optimize a global utility function which integrates both the degree of SLA fulfillment and the operating costs. We resort to a Constraint Pro- gramming approach to formulate and solve the optimization problem. Results obtained through simulations validate our approach