Showing papers by "Angelo Genovese published in 2020"

A Decision Support System for Wind Power Production

Abstract: Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for the traders operating in the energy market and for the managers in charge of planning the nonrenewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze the wind farm (WF) production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on the weather forecasts; thus, it is suitable for the WFs that cannot collect or manage the real-time data acquired by the sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of the WF orography. We tuned and evaluated the proposed system using the data collected from two WFs over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and the impact of our system at ten different WFs under real-world conditions and achieved a significant improvement with respect to their previous approach.

Computational intelligence in cloud computing

Abstract: Cloud Computing (CC) is a model that enables ubiquitous, convenient, and on-demand network access to a shared pool of configurable computing resources. In CC applications, it is possible to access both software and hardware architectures remotely and with little or no knowledge about their physical or logical locations. Due to its low deployment and management costs, the CC paradigm is being increasingly used in a wide variety of online services and applications, including remote computation, software-as-a-service, off-site storage, entertainment, and communication platforms. However, several aspects of CC applications, such as system design, optimization, and security issues, have become too complex to be efficiently treated using traditional algorithmic approaches under the increasingly high complexity and performance demands of current applications. Recently, advances in Computational Intelligence (CI) techniques have fostered the development of intelligent solutions for CC applications. CI methods such as artificial neural networks, deep learning, fuzzy logic, and evolutionary algorithms have enabled improving CC paradigms through their capabilities of extracting knowledge from high quantities of real-world data, thus further optimizing their design, performance, and security with respect to traditional techniques. This chapter introduces recent CI techniques, reviews the main applications of CI in CC, and presents challenges and research trends.

Unsupervised Learning From Limited Available Data by β-NMF and Dual Autoencoder

Abstract: Unsupervised Learning (UL) models are a class of Machine Learning (ML) which concerns with reducing dimensionality, data factorization, disentangling and learning the representations among the data. The UL models gain their popularity due to their abilities to learn without any predefined label, and they are able to reduce the noise and redundancy among the data samples. However, generalizing the UL models for different applications including image generation, compression, encoding, and recognition faces different challenges due to limited available data for learning, diversity, and complex dimensions. To overcome such challenges, we propose a partial learning procedure by utilizing the $\beta$-Non Negative Matrix Factorization ($\beta$-NMF), which maps the data into two complementary subspaces constituting generalized driven priors among the data. Moreover, we employ a dual-shallow Autoencoder (AE) to learn the subspaces separately or jointly for image reconstruction and visualization tasks, where our model performance shows superior results to the literary works when learning the model with a small amount of data and generalizing it for large-scale unseen data.

On Approximating the Non-negative Rank: Applications to Unsupervised Image Reduction

Abstract: Unsupervised Learning (UL) methods are a class of machine learning which aims to disentangle the representations and reduce the dimensionality among the data without any predefined labels Among all UL methods, the Non-negative Matrix Factorization (NMF) factorizes the data into two subspaces of non-negative components Moreover, the NMF enforces the non-negativity, sparsity, and part-based analysis, thus the representations can be interpreted and explained for the Explainable Artificial Intelligence (XAI) applications However, one of the main issues when using the NMF is to impose the factorization rank r to identify the dimensionality of the subspaces, where the rank is usually unknown in advance and known as the non-negative rank that is used as a prior to carrying out the factorization Accordingly, we propose a novel method for the non-negative rank r approximation to help solving this problem, and we generalize our method among different image scales Where, the results on different image data sets confirm the validity of our approach