3-Domain Modelling

The objectives of this chapter are: (1) Presentation of a 3-domain Metasystem framework, which allows the identification of appropriate modelling approaches while considering not only the system but also the multiple study perspectives and planning constraints such as limited expertise or data. (2) Discussion of the 3-domain modelling concept, an integrated concept of different modelling methods used in different domains. (3) Providing a mapping between different modelling approaches and different domains and use cases on the basis of literature discussions and experiences.

[1]  Melissa A. Schilling,et al.  Technology S-Curves in Renewable Energy Alternatives: Analysis and Implications for Industry and Government. , 2009 .

[2]  Hisham El-Shishiny,et al.  An enhanced approach for Trend Impact Analysis , 2008 .

[3]  Arnulf Grubler,et al.  Lessons from the history of technological change for clean energy scenarios and policies , 2011 .

[4]  Hsiao-Tien Pao,et al.  Forecasting electricity market pricing using artificial neural networks , 2007 .

[5]  Rob J Hyndman,et al.  Unmasking the Theta Method , 2003 .

[6]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[7]  Rob J Hyndman,et al.  A state space framework for automatic forecasting using exponential smoothing methods , 2002 .

[8]  Nebojsa Nakicenovic,et al.  Dynamics of energy technologies and global change , 1999 .

[9]  John D. Sterman,et al.  System Dynamics: Systems Thinking and Modeling for a Complex World , 2002 .

[10]  V. Lo Brano,et al.  Forecasting daily urban electric load profiles using artificial neural networks , 2004 .

[11]  Yanuar Nugroho,et al.  Mapping issues and envisaging futures: An evolutionary scenario approach , 2012 .

[12]  A. Grubler,et al.  Future capacity growth of energy technologies: are scenarios consistent with historical evidence? , 2013, Climatic Change.

[13]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Chris Chatfield,et al.  Time series forecasting with neural networks: a comparative study using the air line data , 2008 .

[15]  N Roberts,et al.  Introduction to Computer Simulation—A System Dynamics Modeling Approach , 1994 .

[16]  S. Solomou,et al.  Phases of Economic Growth, 1850-1973: Kondratieff Waves and Kuznets Swings. , 1989 .

[17]  R. Lewandowski.  Practitioners' forum. Sales forecasting by forsys , 1982 .

[18]  Ilan Yaniv,et al.  A case study of expert judgment: Economists' probabilities versus base-rate model forecasts , 1992 .

[19]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[20]  Spyros Makridakis,et al.  The M3-Competition1 , 2000 .

[21]  J. Keith Ord,et al.  Automatic neural network modeling for univariate time series , 2000 .

[22]  Jay Wright Forrester,et al.  Urban Dynamics , 1969 .

[23]  Robert Fildes,et al.  Generalising about univariate forecasting methods: Further empirical evidence , 1998 .

[24]  Jørgen Randers,et al.  Elements of the System Dynamics Method , 1997 .

[25]  Liz Varga,et al.  Energy and complexity: New ways forward , 2015 .

[26]  A. Grubler,et al.  Marginalization of end-use technologies in energy innovation for climate protection , 2012 .

[27]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[28]  Jay Wright Forrester,et al.  Principles of systems : text and workbook, chapters 1 through 10 , 1968 .

[29]  Roland De Guio,et al.  Integrated energy planning in cities and territories: A review of methods and tools , 2013 .

[30]  Madhab C. Bora,et al.  Introduction to System Dynamics Modeling , 1994 .

[31]  Paul Windrum,et al.  Empirical Validation of Agent Based Models: A Critical Survey , 2006 .

[32]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[33]  William Remus,et al.  Neural Network Models for Time Series Forecasts , 1996 .

[34]  Luís Torgo,et al.  Data Mining with R: Learning with Case Studies , 2010 .

[35]  Chia-Yon Chen,et al.  Technology forecasting of new clean energy: The example of hydrogen energy and fuel cell , 2010 .

[36]  M. O'Connor,et al.  Judgemental and statistical time series forecasting: a review of the literature , 1996 .

[37]  Werner A. Stahel,et al.  Forecasting demand for special telephone services: A case study , 1990 .

[38]  Nigel Meade,et al.  Evidence for the selection of forecasting methods , 2000 .

[39]  Michel Godet,et al.  Creating Futures: Scenario Planning as a Strategic Management Tool , 2001 .

[40]  K. Nikolopoulos,et al.  The theta model: a decomposition approach to forecasting , 2000 .

[41]  George J. Klir,et al.  Uncertainty Modeling and Analysis in Engineering and the Sciences (Hardcover) , 2006 .

[42]  Andrew Ford,et al.  Statistical screening of system dynamics models , 2005 .

[43]  R. E. Abdel-Aal,et al.  Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks , 2008, Comput. Ind. Eng..

[44]  Ibrahim El-Amin,et al.  Artificial neural networks as applied to long-term demand forecasting , 1999, Artif. Intell. Eng..

[45]  Sven F. Crone,et al.  Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction , 2011 .

[46]  D. Bunn,et al.  Interaction of judgemental and statistical forecasting methods: issues & , 1991 .

[47]  George J. Klir,et al.  Architecture of Systems Problem Solving , 1985, Springer US.

[48]  Emanuel Parzen,et al.  ARARMA models for time series analysis and forecasting , 1982 .

[49]  J. Gurr,et al.  Understanding complex urban systems : multidisciplinary approaches to modeling , 2014 .

[50]  Sergio Albeverio,et al.  The Dynamics of Complex Urban Systems , 2008 .

[51]  J. Armstrong,et al.  Illusions in Regression Analysis , 2011 .

[52]  David C. Yen,et al.  Diffusion of mobile telephony: An empirical study in Taiwan , 2009 .

[53]  M. I. Sheppard,et al.  Effects of model complexity on uncertainty estimates , 1998 .

[54]  Roland De Guio,et al.  Application of S-shaped curves , 2007 .

[55]  Arnulf Grubler,et al.  Energy transitions research: Insights and cautionary tales , 2012 .

[56]  J. Scott Armstrong,et al.  The Forecasting Canon: Nine Generalizations to Improve Forecast Accuracy , 2005 .

[57]  Fred Collopy,et al.  Causal Forces: Structuring Knowledge for Time-Series Extrapolation , 1993 .

[58]  Fred Collopy,et al.  Automatic Identification of Time Series Features for Rule-Based Forecasting , 2001 .

[59]  Jae Kyu Lee,et al.  UNIK-FCST: knowledge-assisted adjustment of statistical forecasts , 1990 .

[60]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[61]  Klaus Lucas,et al.  Multicriterial optimisation of communal energy supply concepts , 2007 .

[62]  Fred Collopy,et al.  Expert Opinions About Extrapolation and the Mystery of the Overlooked Discontinuities , 1992 .

[63]  Michel Godet,et al.  The Art of Scenarios and Strategic Planning - Tools and Pitfalls , 2000 .

[64]  P. Allen.  Cities and Regions as Self-Organizing Systems: Models of Complexity , 1997 .

[65]  Jack E. Smith,et al.  The Big Picture – trends, drivers, wild cards, discontinuities and weak signals , 2011 .

[66]  Fred Collopy,et al.  How effective are neural networks at forecasting and prediction? A review and evaluation , 1998 .

[67]  M. Godet.  From Anticipation to Action: A Handbook of Strategic Prospective , 1994 .