9,875 research outputs found
A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks
Cell Switch-Off (CSO) is recognized as a promising approach to reduce the
energy consumption in next-generation cellular networks. However, CSO poses
serious challenges not only from the resource allocation perspective but also
from the implementation point of view. Indeed, CSO represents a difficult
optimization problem due to its NP-complete nature. Moreover, there are a
number of important practical limitations in the implementation of CSO schemes,
such as the need for minimizing the real-time complexity and the number of
on-off/off-on transitions and CSO-induced handovers. This article introduces a
novel approach to CSO based on multiobjective optimization that makes use of
the statistical description of the service demand (known by operators). In
addition, downlink and uplink coverage criteria are included and a comparative
analysis between different models to characterize intercell interference is
also presented to shed light on their impact on CSO. The framework
distinguishes itself from other proposals in two ways: 1) The number of
on-off/off-on transitions as well as handovers are minimized, and 2) the
computationally-heavy part of the algorithm is executed offline, which makes
its implementation feasible. The results show that the proposed scheme achieves
substantial energy savings in small cell deployments where service demand is
not uniformly distributed, without compromising the Quality-of-Service (QoS) or
requiring heavy real-time processing
Distributed data cache designs for clustered VLIW processors
Wire delays are a major concern for current and forthcoming processors. One approach to deal with this problem is to divide the processor into semi-independent units referred to as clusters. A cluster usually consists of a local register file and a subset of the functional units, while the L1 data cache typically remains centralized in What we call partially distributed architectures. However, as technology evolves, the relative latency of such a centralized cache will increase, leading to an important impact on performance. In this paper, we propose partitioning the L1 data cache among clusters for clustered VLIW processors. We refer to this kind of design as fully distributed processors. In particular; we propose and evaluate three different configurations: a snoop-based cache coherence scheme, a word-interleaved cache, and flexible LO-buffers managed by the compiler. For each alternative, instruction scheduling techniques targeted to cyclic code are developed. Results for the Mediabench suite'show that the performance of such fully distributed architectures is always better than the performance of a partially distributed one with the same amount of resources. In addition, the key aspects of each fully distributed configuration are explored.Peer ReviewedPostprint (published version
A Machine Learning-Based Framework for Clustering Residential Electricity Load Profiles to Enhance Demand Response Programs
Load shapes derived from smart meter data are frequently employed to analyze
daily energy consumption patterns, particularly in the context of applications
like Demand Response (DR). Nevertheless, one of the most important challenges
to this endeavor lies in identifying the most suitable consumer clusters with
similar consumption behaviors. In this paper, we present a novel machine
learning based framework in order to achieve optimal load profiling through a
real case study, utilizing data from almost 5000 households in London. Four
widely used clustering algorithms are applied specifically K-means, K-medoids,
Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An
empirical analysis as well as multiple evaluation metrics are leveraged to
assess those algorithms. Following that, we redefine the problem as a
probabilistic classification one, with the classifier emulating the behavior of
a clustering algorithm,leveraging Explainable AI (xAI) to enhance the
interpretability of our solution. According to the clustering algorithm
analysis the optimal number of clusters for this case is seven. Despite that,
our methodology shows that two of the clusters, almost 10\% of the dataset,
exhibit significant internal dissimilarity and thus it splits them even further
to create nine clusters in total. The scalability and versatility of our
solution makes it an ideal choice for power utility companies aiming to segment
their users for creating more targeted Demand Response programs.Comment: 29 pages, 19 figure
Forecast-informed power load profiling: A novel approach
Power load forecasting plays a critical role in the context of electric supply optimization. The concept ofload characterization and profiling has been used in the past as a valuable approach to improve forecasting performance as well as problem interpretability. This paper proposes a novel, fully fledged theoretical framework for a joint probabilistic clustering andregression model, which is different from existing models that treat both processes independently. The clustering process is enhanced by simultaneously using the input data and the prediction targets during training. The model is thus capable of obtaining better clusters than other methods, leading to more informativedata profiles, while maintaining or improving predictive performance. Experiments have been conducted using aggregated load data from two U.S.A. regional transmission organizations, collected over 8 years. These experiments confirm that the proposed model achieves the goalsset for interpretability and forecasting performance.This work is partially supported by the National Science Foundation EPSCoR Cooperative Agreement OIA-1757207 and the SpanishMINECO grants TEC2014-52289-R and TEC2017-83838-R
Cascaded clustering analysis of electricity load profile based on smart metering data
13th International Conference on Electrical and Electronics Engineering, ELECO 2021Virtual, Bursa25 November 2021 through 27 November 2021 Code 176537In the operation of deregulated power systems, consumption data is used effectively by system operators. Thanks to the developing measurement and communication technologies, measurement data with high temporal resolution can be obtained from many points within the power systems. Considering the number of consumers connected to power systems, the data in question is a big data. To deal with such a large amount data clustering analyzes are effectively used to identify consumers with similar behaviors in consumption data and to represent consumers with similar behaviors with a single load profile. Success of the clustering studies is related with the compatibility of the data to the selected algorithm and the appropriateness of the adopted approaches to the application of the algorithm to the data. In this study, a cascade clustering algorithm created with the k-medoids algorithm is proposed
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