3,634 research outputs found
2-D convolutional deep neural network for the multivariate prediction of photovoltaic time series
No full textHere, 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
Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series
No full textThe 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
Milano consolato nell' elettione a questo arciuescouado, e promotione alla sagra porpora dell' eminentissimo Federico Visconti : colla sua solennissima entrata seguita a' 11. genaro 1682 e fontioni antecedenti /
No full textFrontispiece coat of arms of Milan, engraved by Federico Agnelli.Signatures: pi⁴ A-G⁴ H⁴(-H4).Mode of access: Internet.Binding: limp vellum. Author & title written on spine
Challenges and Perspectives of Smart Grid Systems in Islands: A Real Case Study
No full textIslands are facing significant challenges in meeting their energy needs in a sustainable, affordable, and reliable way. Traditionally, the primary source of electricity on the islands has been imported diesel fuel, with high financial costs for most utilities. In this context, even replacing part of the traditional production with renewable energy source can reduce costs and improve the quality of life of islanders. However, integrating large amounts of renewable energy production into existing grids introduces many concerns regarding feasibility, economic analysis, and technical implementation. From this point of view, machine learning and deep learning techniques are efficient tools to mitigate these problems. Their potential results are beneficial considering isolated grids of small islands which are not connected to the national grid. In this paper, a study of the Italian island of Ponza is carried out. The isolation leads to several challenges, such as the high cost related to the transport, installation, and maintenance of renewable energy sources in a small area with several constraints and their intermittent power production, which requires the use of storage systems for dispatching purposes. The proposed study aims to identify future developments of the electricity grid by considering the deployment of both renewable energy sources and energy storage systems. Furthermore, future scenarios are depicted through the use of autoregressive and deep learning techniques to give an idea about the economic costs of both energy demand and supply
Time series prediction with autoencoding LSTM networks
No full textNowadays, solving prediction problems in green computing is an open and challenging task, for which solutions based on deep learning are studied. In this work, we present a forecasting algorithm based on Long Short-Term Memory networks applied to renewable energy sources time series prediction. We make use of an encoder-decoder structure to extract useful representative sequence data, employing a stacked LSTM architecture for data embedding and successive prediction. By comparing the performance of the proposed forecasting scheme with a classical twolayer LSTM structure, we are able to asses the performance of the former as a robust tool for solving prediction problems in the green computing framework
»It contained harbours that pleased me like sonnets«. Kleine Poetik der diegetischen Karte
No full textIn this article, Federico Italiano explores the relationship between literature and cartography. Beginning with Stevenson’s Treasure Island, the author frames the topic through a general theoretical lens on the spatial dimension of literary texts. He then focuses on a specific phenomenon of literary "carticity"—the diegetic presence of the map, that is, the map as an integral element of the narrative structure. Among others, Italiano examines the works of Houellebecq and Cormac McCarthy
A Combined Deep Learning Approach for Time Series Prediction in Energy Environments
No full textIn smart grids and microgrids, time series prediction is a fundamental tool for enabling intelligent energy resource management and advanced interactions between heterogeneous agents. In this work, we propose a solution to the energy forecasting problem based on two machine learning techniques: Convolutional Neural Network and Long Short-Term Memory Network. These techniques are combined with a new embedding format to appropriately feed the time series to the stacked network architecture. The resulting novel deep learning scheme is able to retrieve information from the data by inferring time dependent correlation structures. The model is validated using real-world examples, showing good performances with a 3-days forecasting horizon
2-d convolutional deep neural network for the multivariate prediction of photovoltaic time series
No full textHere, 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
Portrait of the Author in (Him/Her)self
No full textA fragment of a philosophical essay by Jean-Luc Nancy and Federico Ferrari titled Iconographie dell’auteur (Paris, 2005), published for the first time in Polish, that addresses the problem of a relationship between the image of the author and his/her work
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