Electricity clustering framework for automatic classification of customer loads

Clustering in energy markets is a top topic with high significance on expert and intelligent systems. The main impact of is paper is the proposal of a new clustering framework for the automatic classification of electricity customers’ loads. An automatic selection of the clustering classification algorithm is also highlighted. Finally, new customers can be assigned to a predefined set of clusters in the classificationphase. The computation time of the proposed framework is less than that of previous classification tech- niques, which enables the processing of a complete electric company sample in a matter of minutes on a personal computer. The high accuracy of the predicted classification results verifies the performance of the clustering technique. This classification phase is of significant assistance in interpreting the results, and the simplicity of the clustering phase is sufficient to demonstrate the quality of the complete mining framework.Ministerio de Economía y Competitividad TEC2013-40767-RMinisterio de Economía y Competitividad IDI- 2015004

Classification of Processes by the Lyapunov exponent

This paper deals with the problem of the discrimination between wellpredictable and not-well-predictable time series. One criterion for the separation is given by the size of the Lyapunov exponent, which was originally defined for deterministic systems. However, the Lyapunov exponent can also be analyzed and used for stochastic time series. Experimental results illustrate the classification between well-predictable and not-well-predictable time series. --

INDEKS SUBJEK VOLUME 30

Agent      -Based Modelling Double Exponential       Smoothing, ABMDES                                      363      Crosslinking-                                                     300      Risk-                                                                   160 Aggregate Risk Potential, ARP                        25, 160 Agroindustri                                                   138, 234      Agrowisata Terintegrasi-, ATA                         138      -Apel                                                                  341      -Buah                                                                 338      -Kopi                                                                  207      -Nilam                                                                  53 Alas Kaki                                                                  43 Analisi

INDEKS SUBJEK VOLUME 30

Agent      -Based Modelling Double Exponential       Smoothing, ABMDES                                      363      Crosslinking-                                                     300      Risk-                                                                   160 Aggregate Risk Potential, ARP                        25, 160 Agroindustri                                                   138, 234      Agrowisata Terintegrasi-, ATA                         138      -Apel                                                                  341      -Buah                                                                 338      -Kopi                                                                  207      -Nilam                                                                  53 Alas Kaki                                                                  43 Analisi

Classification of Processes by the Lyapunov exponent

This paper deals with the problem of the discrimination between well-predictable and not-well-predictable time series. One criterion for the separation is given by the size of the Lyapunov exponent, which was originally defined for deterministic systems. However, the Lyapunov exponent can also be analyzed and used for stochastic time series. Experimental results illustrate the classification between well-predictable and not-well-predictable time series

Collective Invention during the British Industrial Revolution The Case of the Cornish Pumping Engine

In this paper, we argue that together with individual inventors and firms, what Robert C. Allen (1983) has termed as collective invention settings (that is settings in which rival firms freely release each other pertinent technical information), were also a crucial source of innovation in the industrial revolution period. Until now, this has been very little considered in the literature. This paper focuses on one of these cases: the Cornish mining district. In Cornwall, during the early nineteenth century, a notable collective invention setting, gradually emerged. This case is particularly remarkable because it was capable of generating a continuous and sustained flow of improvements in steam pumping technology which in the end greatly contributed to improve the thermodynamic efficiency of the steam engine. In this paper we study in detail the specific economic circumstances that led to the formation of this collective invention setting and we analyses its consequences for the rate of technological innovationCollective inventions, information sharing, case study

R&D Investment, Market Structure, and Industry Growth

We study how alternative market structures influence market supply and R&D investment decisions of firms operating in dynamic imperfectly competitive environments. Firms can reduce their future production cost through R&D investment today, which is the engine of endogenous industry growth. Our framework enables us to identify key strategic ingredients in firms dynamic competitive behavior through analytical characterizations. These ingredients are a static market externality, stemming from the standard oligopolistic Cournot competition, a dynamic externality that arises due to knowledge spillovers, and a dynamic market externality that comes from the interaction of knowledge spillovers with future market oligopolistic competition that firms internalize while making decisions. We isolate the impact of each strategic ingredient by comparing four alternative market structures.R&D investment, Cournot competition, oligopolistic non-cooperative dynamic games

Feature Selection in Large Scale Data Stream for Credit Card Fraud Detection

There is increased interest in accurate model acquisition from large scale data streams. In this paper, because we have focused attention on time-oriented variation, we propose a method contracting time-series data for data stream. Additionally, our proposal method employs the combination of plural simple contraction method and original features. In this experiment, we treat a real data stream in credit card transactions because it is large scale and difficult to classify. This experiment yields that this proposal method improves classification performance according to training data. However, this proposal method needs more generality. Hence, we'll improve generality with employing the suitable combination of a contraction method and a feature for the feature in our proposal method

QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns

Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity.Comment: To appear in the Proceedings of WWW 2015. 11pp, 10 fig. Interactive visualization, data, and other info available at http://snap.stanford.edu/quotus

The application of data mining by classification in a database of notified covid-19 cases in Manaus-AM

This scientific article aims to present information on the cases of comorbidity that most aggravate the symptoms of SARS-CoV-2 (Covid 19) with data extracted from the database of the official website of the Ministry of Health, which defined a system to monitor the information detected in the diagnoses of each patient. Since the beginning of the pandemic, the city of Manaus has suffered great consequences in relation to the SARS-CoV-2 virus (Covid-19). predicting patients at higher risk of death. We describe the origin and spread of the virus and the use of the SGBD software MySql and MySql Workbench to improve data in the selection and pre-processing, with the resources of the weka tool for knowledge learning, ending with the objective achieved in the classification of comorbidities that further aggravate the clinical conditions