%0 Journal Article %J Renewable Energy %D 2016 %T Wind speed forecasting for wind farms: A method based on support vector regression %A Santamaría-Bonfil, G. %A Reyes-Ballesteros, A. %A Gershenson, C. %K Genetic algorithms %K Non-linear analysis %K Phase space reconstruction %K Support vector regression %K Wind speed forecasting %X In this paper, a hybrid methodology based on Support Vector Regression for wind speed forecasting is proposed. Using the autoregressive model called Time Delay Coordinates, feature selection is performed by the Phase Space Reconstruction procedure. Then, a Support Vector Regression model is trained using univariate wind speed time series. Parameters of Support Vector Regression are tuned by a genetic algorithm. The proposed method is compared against the persistence model, and autoregressive models (AR, ARMA, and ARIMA) tuned by Akaike's Information Criterion and Ordinary Least Squares method. The stationary transformation of time series is also evaluated for the proposed method. Using historical wind speed data from the Mexican Wind Energy Technology Center (CERTE) located at La Ventosa, Oaxaca, México, the accuracy of the proposed forecasting method is evaluated for a whole range of short termforecasting horizons (from 1 to 24 h ahead). Results show that, forecasts made with our method are more accurate for medium (5–23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models. %B Renewable Energy %V 85 %P 790–809 %8 1 %@ 0960-1481 %G eng %U http://www.sciencedirect.com/science/article/pii/S0960148115301014 %R http://dx.doi.org/10.1016/j.renene.2015.07.004 %0 Journal Article %J Complexity %D 2015 %T When slower is faster %A Gershenson, Carlos %A Helbing, Dirk %K cascading effects %K collective motion %K Evolution %K phase transitions %X The slower is faster (SIF) effect occurs when a system performs worse as its components try to do better. Thus, a moderate individual efficiency actually leads to a better systemic performance. The SIF effect takes place in a variety of phenomena. We review studies and examples of the SIF effect in pedestrian dynamics, vehicle traffic, traffic light control, logistics, public transport, social dynamics, ecological systems, and adaptation. Drawing on these examples, we generalize common features of the SIF effect and suggest possible future lines of research. {\copyright} 2015 Wiley Periodicals, Inc. Complexity 21: 9–15, 2015 %B Complexity %V 21 %P 9–15 %G eng %U http://arxiv.org/abs/1506.06796 %R 10.1002/cplx.21736 %0 Book Section %B Unifying Themes in Complex Systems %D 2012 %T The World as Evolving Information %A Carlos Gershenson %E Minai, Ali %E Braha, Dan %E Yaneer {Bar-Yam} %X This paper discusses the benefits of describing the world as information, especially in the study of the evolution of life and cognition. Traditional studies encounter problems because it is difficult to describe life and cognition in terms of matter and energy, since their laws are valid only at the physical scale. However, if matter and energy, as well as life and cognition, are described in terms of information, evolution can be described consistently as information becoming more complex. The paper presents five tentative laws of information, valid at multiple scales, which are generalizations of Darwinian, cybernetic, thermodynamic, and complexity principles. These are further used to discuss the notions of life and cognition and their evolution. %B Unifying Themes in Complex Systems %I Springer %C Berlin Heidelberg %V VII %P 100-115 %G eng %U http://arxiv.org/abs/0704.0304 %R 10.1007/978-3-642-18003-3_10 %0 Journal Article %J International Journal of Artificial Life Research %D 2011 %T What does artificial life tell us about death? %A Carlos Gershenson %X Short philosophical essay %B International Journal of Artificial Life Research %V 2 %P 1-5 %G eng %U http://arxiv.org/abs/0906.2824 %0 Journal Article %J {PLoS ONE} %D 2009 %T Why does public transport not arrive on time? The pervasiveness of equal headway instability %A Carlos Gershenson %A Luis A. Pineda %X Background The equal headway instability phenomenon is pervasive in public transport systems. This instability is characterized by an aggregation of vehicles that causes inefficient service. While equal headway instability is common, it has not been studied independently of a particular scenario. However, the phenomenon is apparent in many transport systems and can be modeled and rectified in abstraction. Methodology We present a multi-agent simulation where a default method with no restrictions always leads to unstable headways. We discuss two methods that attempt to achieve equal headways, called minimum and maximum. Since one parameter of the methods depends on the passenger density, adaptive versions–-where the relevant parameter is adjusted automatically–-are also put forward. Our results show that the adaptive maximum method improves significantly over the default method. The model and simulation give insights of the interplay between transport design and passenger behavior. Finally, we provide technological and social suggestions for engineers and passengers to help achieve equal headways and thus reduce delays. Conclusions The equal headway instability phenomenon can be avoided with the suggested technological and social measures. %B {PLoS ONE} %V 4 %P e7292 %G eng %U http://dx.doi.org/10.1371/journal.pone.0007292 %R 10.1371/journal.pone.0007292 %0 Conference Paper %B Advances in Artificial Life, 7th European Conference, {ECAL} 2003 {LNAI} 2801 %D 2003 %T When Can We Call a System Self-Organizing? %A Carlos Gershenson %A Francis Heylighen %E Banzhaf, W %E T. Christaller %E P. Dittrich %E J. T. Kim %E J. Ziegler %X We do not attempt to provide yet another definition of self-organizing systems, nor review previous definitions. We explore the conditions necessary to describe self-organizing systems, inspired on decades of their study, in order to understand them better. These involve the dynamics of the system, and the purpose, boundaries, and description level chosen by an observer. We show how, changing the level or ``graining'' of description, the same system can be self-organizing or not. We also discuss common problems we face when studying self-organizing systems. We analyse when building, designing, and controlling artificial self-organizing systems is useful. We state that self-organization is a way of observing systems, not a class of systems. %B Advances in Artificial Life, 7th European Conference, {ECAL} 2003 {LNAI} 2801 %I Springer %C Berlin %P 606–614 %G eng %U http://arxiv.org/abs/nlin.AO/0303020 %0 Unpublished Work %D 2002 %T Where is the problem of ``Where is the mind?''? %A Carlos Gershenson %X We propose that the discussions about ``where the mind is'' depend directly on the metaphysical preconception and definition of ``mind''. If we see the mind from one perspective (individualist), it will be only in the brain, and if we see it from another (active externalist), it will be embedded in the body and extended into the world. The ``whereabouts'' of the mind depends on our 1 of mind. Therefore, we should not ask if the mind is somewhere, but if it is somehow. %G eng %U http://cogprints.org/2620/