59 research outputs found
Prediction in Photovoltaic Power by Neural Networks
The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches
Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series
The large-scale penetration of renewable energy sources is forcing the transition towards
the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new
methodologies for the dynamic energy management of distributed energy resources and foster to form
partnerships and overcome integration barriers. The prediction of energy production of renewable energy
sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool
in the modern management of electrical grids shifting from reactive to proactive, with also the help of
advanced monitoring systems, data analytics and advanced demand side management programs. The gradual
move towards a smart grid environment impacts not only the operating control/management of the grid, but
also the electricity market. The focus of this article is on advanced methods for predicting photovoltaic
energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system
management, even at prosumer's level and for improving the resilience of smart grids. Four different deep
neural models for the multivariate prediction of energy time series are proposed; all of them are based on the
Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term
dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher
levels of abstraction, since they allow to combine and filter different time series considering all the available
information. The proposed models are applied to real-world energy problems to assess their performance
and they are compared with respect to the classic univariate approach that is used as a reference benchmark.
The significance of this work is to show that, once trained, the proposed deep neural networks ensure their
applicability in real online scenarios characterized by high variability of data, without requiring retraining
and end-user's tricks
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks
A change of the prevalent supervised learning techniques is foreseeable in
the near future: from the complex, computational expensive algorithms to more
flexible and elementary training ones. The strong revitalization of randomized
algorithms can be framed in this prospect steering. We recently proposed a
model for distributed classification based on randomized neural networks and
hyperdimensional computing, which takes into account cost of information
exchange between agents using compression. The use of compression is important
as it addresses the issues related to the communication bottleneck, however,
the original approach is rigid in the way the compression is used. Therefore,
in this work, we propose a more flexible approach to compression and compare it
to conventional compression algorithms, dimensionality reduction, and
quantization techniques.Comment: 12 pages, 3 figure
Multi-damage detection in composite space structures via deep learning
The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples
2-D convolutional deep neural network for the multivariate prediction of photovoltaic time series
Here, we propose a new deep learning scheme to solve the energy time series prediction
problem. The model implementation is based on the use of Long Short-Term Memory networks
and Convolutional Neural Networks. These techniques are combined in such a fashion that interdependencies
among several different time series can be exploited and used for forecasting purposes
by filtering and joining their samples. The resulting learning scheme can be summarized as a
superposition of network layers, resulting in a stacked deep neural architecture. We proved the
accuracy and robustness of the proposed approach by testing it on real-world energy problems
Lung ultrasound in systemic sclerosis: correlation with high-resolution computed tomography, pulmonary function tests and clinical variables of disease
Interstitial lung disease (ILD) is a hallmark of systemic sclerosis (SSc). Although high-resolution computed tomography (HRCT) is the gold standard to diagnose ILD, recently lung ultrasound (LUS) has emerged in SSc patients as a new promising technique for the ILD evaluation, noninvasive and radiation-free. The aim of this study was to evaluate if there is a correlation between LUS, chest HRCT, pulmonary function tests findings and clinical variables of the disease. Thirty-nine patients (33 women and 6 men; mean age 51 ± 15.2 years) underwent clinical examination, HRCT, pulmonary function tests and LUS for detection of B-lines. A positive correlation exists between the number of B-lines and the HRCT score (r = 0.81, p < 0.0001), conversely a negative correlation exists between the number of B-lines and diffusing capacity of the lung for carbon monoxide (DLCO) (r = −0.63, p < 0.0001). The number of B-lines increases along with the progression of the capillaroscopic damage. A statistically significant difference in the number of B-lines was found between patients with and without digital ulcers [42 (3–84) vs 16 (4–55)]. We found that the number of B-lines increased with the progression of both HRCT score and digital vascular damage. LUS may therefore, be a useful tool to determine the best timing for HRCT execution, thus, preventing for many patients a continuous and useless exposure to ionizing radiatio
Perceptron theory can predict the accuracy of neural networks
Multilayer neural networks set the current state of
the art for many technical classification problems. But, these
networks are still, essentially, black boxes in terms of analyzing
them and predicting their performance. Here, we develop a
statistical theory for the one-layer perceptron and show that
it can predict performances of a surprisingly large variety of
neural networks with different architectures. A general theory
of classification with perceptrons is developed by generalizing
an existing theory for analyzing reservoir computing models
and connectionist models for symbolic reasoning known as
vector symbolic architectures. Our statistical theory offers three
formulas leveraging the signal statistics with increasing detail.
The formulas are analytically intractable, but can be evaluated
numerically. The description level that captures maximum details
requires stochastic sampling methods. Depending on the network
model, the simpler formulas already yield high prediction accuracy.
The quality of the theory predictions is assessed in three
experimental settings, a memorization task for echo state networks
(ESNs) from reservoir computing literature, a collection of
classification datasets for shallow randomly connected networks,
and the ImageNet dataset for deep convolutional neural networks.
We find that the second description level of the perceptron theory
can predict the performance of types of ESNs, which could not
be described previously. Furthermore, the theory can predict
deep multilayer neural networks by being applied to their output
layer. While other methods for prediction of neural networks
performance commonly require to train an estimator model,
the proposed theory requires only the first two moments of
the distribution of the postsynaptic sums in the output neurons.
Moreover, the perceptron theory compares favorably to other
methods that do not rely on training an estimator model
A review of the enabling methodologies for knowledge discovery from smart grids data
The large-scale deployment of pervasive sensors and decentralized computing in modern smart
grids is expected to exponentially increase the volume of data exchanged by power system applications.
In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions
in a data rich, but information limited environment represents a relevant issue to address. To this aim,
this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing,
exploring its various application fields by considering the power system stakeholder available data and
knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors
summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms
for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the
IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors
propose the development of a data-driven forecasting methodology, which is modeled by considering
the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described
methodology is applied to solve the load forecasting problem for a complex user case, in order to
emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich
environments, as feedback for the improvement of the forecasting performances
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