330 research outputs found
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting
Solar radiation prediction is an important challenge for the electrical
engineer because it is used to estimate the power developed by commercial
photovoltaic modules. This paper deals with the problem of solar radiation
prediction based on observed meteorological data. A 2-day forecast is obtained
by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS
are used to exploit the correlation between solar radiation and
timescale-related variations of wind speed, humidity, and temperature. The
input to the selected WRNN is provided by timescale-related bands of wavelet
coefficients obtained from meteorological time series. The experimental setup
available at the University of Catania, Italy, provided this information. The
novelty of this approach is that the proposed WRNN performs the prediction in
the wavelet domain and, in addition, also performs the inverse wavelet
transform, giving the predicted signal as output. The obtained simulation
results show a very low root-mean-square error compared to the results of the
solar radiation prediction approaches obtained by hybrid neural networks
reported in the recent literature
A hybrid neuro--wavelet predictor for QoS control and stability
For distributed systems to properly react to peaks of requests, their
adaptation activities would benefit from the estimation of the amount of
requests. This paper proposes a solution to produce a short-term forecast based
on data characterising user behaviour of online services. We use \emph{wavelet
analysis}, providing compression and denoising on the observed time series of
the amount of past user requests; and a \emph{recurrent neural network} trained
with observed data and designed so as to provide well-timed estimations of
future requests. The said ensemble has the ability to predict the amount of
future user requests with a root mean squared error below 0.06\%. Thanks to
prediction, advance resource provision can be performed for the duration of a
request peak and for just the right amount of resources, hence avoiding
over-provisioning and associated costs. Moreover, reliable provision lets users
enjoy a level of availability of services unaffected by load variations
Multi-time-horizon Solar Forecasting Using Recurrent Neural Network
The non-stationarity characteristic of the solar power renders traditional
point forecasting methods to be less useful due to large prediction errors.
This results in increased uncertainties in the grid operation, thereby
negatively affecting the reliability and increased cost of operation. This
research paper proposes a unified architecture for multi-time-horizon
predictions for short and long-term solar forecasting using Recurrent Neural
Networks (RNN). The paper describes an end-to-end pipeline to implement the
architecture along with the methods to test and validate the performance of the
prediction model. The results demonstrate that the proposed method based on the
unified architecture is effective for multi-horizon solar forecasting and
achieves a lower root-mean-squared prediction error compared to the previous
best-performing methods which use one model for each time-horizon. The proposed
method enables multi-horizon forecasts with real-time inputs, which have a high
potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE
2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i
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
Improving files availability for BitTorrent using a diffusion model
The BitTorrent mechanism effectively spreads file fragments by copying the
rarest fragments first. We propose to apply a mathematical model for the
diffusion of fragments on a P2P in order to take into account both the effects
of peer distances and the changing availability of peers while time goes on.
Moreover, we manage to provide a forecast on the availability of a torrent
thanks to a neural network that models the behaviour of peers on the P2P
system. The combination of the mathematical model and the neural network
provides a solution for choosing file fragments that need to be copied first,
in order to ensure their continuous availability, counteracting possible
disconnections by some peers
Power Forecasting in Photovoltaic System using Hybrid ANN and Wavelet Transform based Method
Solar energy is a sustainable, renewable energy which is a part of latest industry standards of operation in line with industry 4.0. Solar power variability leads to fluctuation and uncertainty in Photovoltaic (PV) output power. It is a significant issue with regard to the high penetration of PV power generation. The solar irradiance is affected by weather conditions, and varies with geographical locations. Accurate PV power output forecasting is essential for the planning and scheduling alternate sources of conventional power. In this paper we propose a frequency domain approach for forecasting of short-term PV output power. The wavelet transform allows identification of periodic components with time localization, whereas the Artificial Neural Network (ANN) technique allows us to model the non-linearities in the PV time series. In this paper, PV power data for the city Bareilly, Uttar Pradesh is forecasted. Numerical simulations show that the proposed forecasting method for PV power output, shows a significant increase in accuracy over other similar methods. The root Mean Square Error, Mean Absolute Error for the proposed method are also calculated and compared with state-of-the art methods for PV power forecasting
Two-Tier Prediction of Solar Power Generation with Limited Sensing Resource
This paper considers a typical solar installations scenario with limited
sensing resources. In the literature, there exist either day-ahead solar
generation prediction methods with limited accuracy, or high accuracy short
timescale methods that are not suitable for applications requiring longer term
prediction. We propose a two-tier (global-tier and local-tier) prediction
method to improve accuracy for long term (24 hour) solar generation prediction
using only the historical power data. In global-tier, we examine two popular
heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN).
In local-tier, the global-tier results are adaptively updated using real-time
analytical residual analysis. The proposed method is validated using the UCLA
Microgrid with 35kW of solar generation capacity. Experimental results show
that the proposed two-tier prediction method achieves higher accuracy compared
to day-ahead predictions while providing the same prediction length. The
difference in the overall prediction performance using either weighted k-NN
based or NN based in the global-tier are carefully discussed and reasoned. Case
studies with a typical sunny day and a cloudy day are carried out to
demonstrate the effectiveness of the proposed two-tier predictions
Estimation of Photovoltaic Generation Forecasting Models using Limited Information
This work deals with the problem of estimating a photovoltaic generation
forecasting model in scenarios where measurements of meteorological variables
(i.e. solar irradiance and temperature) at the plant site are not available. A
novel algorithm for the estimation of the parameters of the well-known PVUSA
model of a photovoltaic plant is proposed. Such a method is characterized by a
low computational complexity, and efficiently exploits only power generation
measurements, a theoretical clear-sky irradiance model, and temperature
forecasts provided by a meteorological service. An extensive experimental
validation of the proposed method on real data is also presented
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