TY - JOUR T1 - Wind speed forecasting for wind farms: A method based on support vector regression JF - Renewable Energy Y1 - 2016 A1 - Santamaría-Bonfil, G. A1 - Reyes-Ballesteros, A. A1 - Gershenson, C. KW - Genetic algorithms KW - Non-linear analysis KW - Phase space reconstruction KW - Support vector regression KW - Wind speed forecasting AB - 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. VL - 85 SN - 0960-1481 UR - http://www.sciencedirect.com/science/article/pii/S0960148115301014 ER - TY - JOUR T1 - When slower is faster JF - Complexity Y1 - 2015 A1 - Gershenson, Carlos A1 - Helbing, Dirk KW - cascading effects KW - collective motion KW - Evolution KW - phase transitions AB - 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 VL - 21 UR - http://arxiv.org/abs/1506.06796 ER - TY - CHAP T1 - The World as Evolving Information T2 - Unifying Themes in Complex Systems Y1 - 2012 A1 - Carlos Gershenson ED - Minai, Ali ED - Braha, Dan ED - Yaneer {Bar-Yam} AB - 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. JF - Unifying Themes in Complex Systems PB - Springer CY - Berlin Heidelberg VL - VII UR - http://arxiv.org/abs/0704.0304 ER - TY - JOUR T1 - What does artificial life tell us about death? JF - International Journal of Artificial Life Research Y1 - 2011 A1 - Carlos Gershenson AB - Short philosophical essay VL - 2 UR - http://arxiv.org/abs/0906.2824 ER - TY - JOUR T1 - Why does public transport not arrive on time? The pervasiveness of equal headway instability JF - {PLoS ONE} Y1 - 2009 A1 - Carlos Gershenson A1 - Luis A. Pineda AB - 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. VL - 4 UR - http://dx.doi.org/10.1371/journal.pone.0007292 ER - TY - CONF T1 - When Can We Call a System Self-Organizing? T2 - Advances in Artificial Life, 7th European Conference, {ECAL} 2003 {LNAI} 2801 Y1 - 2003 A1 - Carlos Gershenson A1 - Francis Heylighen ED - Banzhaf, W ED - T. Christaller ED - P. Dittrich ED - J. T. Kim ED - J. Ziegler AB - 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. JF - Advances in Artificial Life, 7th European Conference, {ECAL} 2003 {LNAI} 2801 PB - Springer CY - Berlin UR - http://arxiv.org/abs/nlin.AO/0303020 ER - TY - UNPB T1 - Where is the problem of ``Where is the mind?''? Y1 - 2002 A1 - Carlos Gershenson AB - 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. UR - http://cogprints.org/2620/ N1 - POCS Essay, COGS, University of Sussex ER -