162,454 research outputs found
Wind Power Forecasting Methods Based on Deep Learning: A Survey
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics
Analysis of Intel's Haswell Microarchitecture Using The ECM Model and Microbenchmarks
This paper presents an in-depth analysis of Intel's Haswell microarchitecture
for streaming loop kernels. Among the new features examined is the dual-ring
Uncore design, Cluster-on-Die mode, Uncore Frequency Scaling, core improvements
as new and improved execution units, as well as improvements throughout the
memory hierarchy. The Execution-Cache-Memory diagnostic performance model is
used together with a generic set of microbenchmarks to quantify the efficiency
of the microarchitecture. The set of microbenchmarks is chosen such that it can
serve as a blueprint for other streaming loop kernels.Comment: arXiv admin note: substantial text overlap with arXiv:1509.0311
Managing Dynamic Enterprise and Urgent Workloads on Clouds Using Layered Queuing and Historical Performance Models
The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic-urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: i.) comparatively evaluate the layered queuing and historical techniques; ii.) evaluate the effectiveness of the management algorithm in different operating scenarios; and iii.) provide guidance on using prediction-based workload and resource management
Speculative Thread Framework for Transient Management and Bumpless Transfer in Reconfigurable Digital Filters
There are many methods developed to mitigate transients induced when abruptly
changing dynamic algorithms such as those found in digital filters or
controllers. These "bumpless transfer" methods have a computational burden to
them and take time to implement, causing a delay in the desired switching time.
This paper develops a method that automatically reconfigures the computational
resources in order to implement a transient management method without any delay
in switching times. The method spawns a speculative thread when it predicts if
a switch in algorithms is imminent so that the calculations are done prior to
the switch being made. The software framework is described and experimental
results are shown for a switching between filters in a filter bank.Comment: 6 pages, 7 figures, to be presented at American Controls Conference
201
- …