Predictive modeling of PV energy production: How to set up the learning task for a better prediction?

In this paper, we tackle the problem of power prediction of several photovoltaic (PV) plants spread over an extended geographic area and connected to a power grid. The paper is intended to be a comprehensive study of one-day ahead forecast of PV energy production along several dimensions of analysis: i) The consideration of the spatio-temporal autocorrelation, which characterizes geophysical phenomena, to obtain more accurate predictions.ii) The learning setting to be considered, i.e. using simple output prediction for each hour or structured output prediction for each day. iii) The learning algorithms: We compare artificial neural networks, most often used for PV prediction forecast, and regression trees for learning adaptive models. The results obtained on two PV power plant datasets show that: taking into account spatio/temporal autocorrelation is beneficial; the structured output prediction setting significantly outperforms the non-structured output prediction setting; and regression trees provide better models than artificial neural networks

A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico

© 2013 IEEE. The mosquito-borne dengue fever is a major public health problem in tropical countries, where it is strongly conditioned by climate factors such as temperature. In this paper, we formulate a holistic machine learning strategy to analyze the temporal dynamics of temperature and dengue data and use this knowledge to produce accurate predictions of dengue, based on temperature on an annual scale. The temporal dynamics are extracted from historical data by utilizing a novel multi-stage combination of auto-encoding, window-based data representation and trend-based temporal clustering. The prediction is performed with a trend association-based nearest neighbour predictor. The effectiveness of the proposed strategy is evaluated in a case study that comprises the number of dengue and dengue hemorrhagic fever cases collected over the period 1985-2010 in 32 federal states of Mexico. The empirical study proves the viability of the proposed strategy and confirms that it outperforms various state-of-the-art competitor methods formulated both in regression and in time series forecasting analysis

ECHAD: Embedding-Based Change Detection from Multivariate Time Series in Smart Grids

Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this article, we propose a novel change detection method, called ECHAD (Embedding-based CHAnge Detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with state-of-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections

Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems

ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs

The outcome-oriented predictive process monitoring is a family of predictive process mining techniques that have witnessed rapid development and increasing adoption in the past few years. Boosted by the recent successful applications of deep learning in predictive process mining, we propose ORANGE, a novel deep learning method for learning outcome-oriented predictive process models. The main innovation of this study is that we adopt an imagery representation of the ongoing traces, which delineates potential data patterns that arise at neighbour pixels. Leveraging a collection of images representing ongoing traces, we train a Convolutional Neural Network (CNN) to predict the outcome of an ongoing trace. The empirical study shows the feasibility of the proposed method by investigating its accuracy on different benchmark outcome prediction problems in comparison to state-of-art competitor methods. In addition, we show how ORANGE can be integrated as an Intelligent Assistant into a CVM realized by MTM Project srl company to support sales agents in their negotiations. This case study shows that ORANGE can be effectively used to smartly monitor the outcome of ongoing negotiations by early highlighting negotiations that are candidate to be completed successfully

Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

Angular moments of the decay Λb 0 → Λμ + μ − at low hadronic recoil

An analysis of the angular distribution of the decay Λ0 b → Λµ +µ − is presented, using data collected with the LHCb detector between 2011 and 2016 and corresponding to an integrated luminosity of approximately 5 fb−1 . Angular observables are determined using a moment analysis of the angular distribution at low hadronic recoil, corresponding to the dimuon invariant mass squared range 15 < q2 < 20 GeV2/c4 . The full basis of observables is measured for the first time. The lepton-side, hadron-side and combined forward-backward asymmetries of the decay are determined to b

Effectively Solving NP-SPEC Encodings by Translation to ASP

NP-SPEC is a language for specifying problems in NP in a declarative way. Despite the fact that the semantics of the language was given by referring to Datalog with circumscription, which is very close to ASP, so far the only existing implementations are by means of ECLiPSe Prolog and via Boolean satisfiability solvers. In this paper, we present translations from NP-SPEC into ASP, and provide an experimental evaluation of existing implementations and the proposed translations to ASP using various ASP solvers. The results show that translating to ASP clearly has an edge over the existing translation into SAT, which involves an intrinsic grounding process. We also argue that it might be useful to incorporate certain language constructs of NPSPEC into mainstream ASP

Multi-Channel Deep Feature Learning for Intrusion Detection

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