21,695 research outputs found
An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning
For short-term solar irradiance forecasting, the traditional point
forecasting methods are rendered less useful due to the non-stationary
characteristic of solar power. The amount of operating reserves required to
maintain reliable operation of the electric grid rises due to the variability
of solar energy. The higher the uncertainty in the generation, the greater the
operating-reserve requirements, which translates to an increased cost of
operation. In this research work, we propose a unified architecture for
multi-time-scale predictions for intra-day solar irradiance forecasting using
recurrent neural networks (RNN) and long-short-term memory networks (LSTMs).
This paper also lays out a framework for extending this modeling approach to
intra-hour forecasting horizons thus, making it a multi-time-horizon
forecasting approach, capable of predicting intra-hour as well as intra-day
solar irradiance. We develop an end-to-end pipeline to effectuate the proposed
architecture. The performance of the prediction model is tested and validated
by the methodical implementation. The robustness of the approach is
demonstrated with case studies conducted for geographically scattered sites
across the United States. The predictions demonstrate that our proposed unified
architecture-based approach is effective for multi-time-scale solar forecasts
and achieves a lower root-mean-square prediction error when benchmarked against
the best-performing methods documented in the literature that use separate
models for each time-scale during the day. Our proposed method results in a
71.5% reduction in the mean RMSE averaged across all the test sites compared to
the ML-based best-performing method reported in the literature. Additionally,
the proposed method enables multi-time-horizon forecasts with real-time inputs,
which have a significant potential for practical industry applications in the
evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Improved success rate and stability for phase retrieval by including randomized overrelaxation in the hybrid input output algorithm
In this paper, we study the success rate of the reconstruction of objects of
finite extent given the magnitude of its Fourier transform and its geometrical
shape. We demonstrate that the commonly used combination of the hybrid input
output and error reduction algorithm is significantly outperformed by an
extension of this algorithm based on randomized overrelaxation. In most cases,
this extension tremendously enhances the success rate of reconstructions for a
fixed number of iterations as compared to reconstructions solely based on the
traditional algorithm. The good scaling properties in terms of computational
time and memory requirements of the original algorithm are not influenced by
this extension.Comment: 14 pages, 8 figure
A multi-scale multi-frequency deconvolution algorithm for synthesis imaging in radio interferometry
Aims : We describe MS-MFS, a multi-scale multi-frequency deconvolution
algorithm for wide-band synthesis-imaging, and present imaging results that
illustrate the capabilities of the algorithm and the conditions under which it
is feasible and gives accurate results.
Methods : The MS-MFS algorithm models the wide-band sky-brightness
distribution as a linear combination of spatial and spectral basis functions,
and performs image-reconstruction by combining a linear-least-squares approach
with iterative minimization. This method extends and combines the
ideas used in the MS-CLEAN and MF-CLEAN algorithms for multi-scale and
multi-frequency deconvolution respectively, and can be used in conjunction with
existing wide-field imaging algorithms. We also discuss a simpler hybrid of
spectral-line and continuum imaging methods and point out situations where it
may suffice.
Results : We show via simulations and application to multi-frequency VLA data
and wideband EVLA data, that it is possible to reconstruct both spatial and
spectral structure of compact and extended emission at the continuum
sensitivity level and at the angular resolution allowed by the highest sampled
frequency.Comment: 17 pages, 11 figure
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