Cilt 1, Sayı 2, Sayfalar 75 - 101 2017-12-03

An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S

Burak Omer Saracoglu [1]

12 23

Our World gives several symptoms of climate change. Devastating draughts increase (negative for World (-)), global mean temperature increase (-), lightning strikes increase (-), sea ice cover melt (-), tree mortality increase (-), and forest degradation increase (-) have been observed for decades. They are all negative measures for continuity of life. Diversity of species has been decreasing, so that life on Earth is dying. Only responsible specie for this situation is humankind. This study presents a small footstep to prevent this situation. Modeling of a 100% renewable power grid on World (Global Grid) is eminent. Annual peak power load (Gigawatt: GW, Kilowatt: kW) (peak demand or load) forecasting in power demand side is crucial for global grid modeling. This study presents an experimental fuzzy inference system for the third core module (100 years’ power demand forecasting) of the first console (long term prediction) of Global Grid Peak Power Prediction System (G2P3S). The inputs (world population, global annual temperature anomalies °C) and the output (annual peak power load demand of Global Grid in GW) are modeled with seven triangular fuzzy input membership functions and seven constant output membership functions. The constant Sugeno-Type fuzzy inference system is used in the current experimental model. The maximum absolute percentage error (MAP) is calculated as 45%, and the mean absolute percentage error (MAPE) is found as 39% in this experimental study. The MAP and MAPE of the first core module model (Type 1) were 0,46 and 0,36. The MAP and MAPE of the second core module model (Interval Type 2) were 0,46 and 0,36. As a result, this study is a good start for the third core module of the first console on Global Grid Peak Power Prediction System research, development, demonstration, & deployment (RD3) project. This experimental study also warns humankind in this subject. Hopefully, the most polluting societies on our World such as China, United States, India, Russia, Japan, Germany, South Korea, and Canada take urgent actions to start to build the foundations of 100% renewable power global grid by organizing a global grid consortium.
Scilab,Sugeno,Takagi-Sugeno-Kang,Fuzzy inference system,Global grid,Peak power
  • Anderegg, W.R., Plavcova, L., Anderegg, L.D., Hacke, U.G., Berry, J.A., Field, C.B. Drought's legacy: multiyear hydraulic deterioration underlies widespread aspen forest die-off and portends increased future risk, Global Change Biology 2013; 19(4): 1188–1196.
  • Hanewinkel, M., Cullmann, D.A., Schelhaas, M.J., Nabuurs, G.J., Zimmermann, N.E. Climate change may cause severe loss in the economic value of European forest land, Nature Climate Change 2013, 3(3), 203-207.
  • Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M., Toulmin, C. Food security: The challenge of feeding 9 billion people. Science 2010, 327(5967), 812–818.
  • Vorosmarty, C.J., Green, P., Salisbury, J., Lammers, R.B. Global water resources: Vulnerability from climate change and population growth. Science 2000, 289(5477).
  • Delpla, I., Jung, A.V., Baures, E., Clement, M., Thomas, O. Impacts of climate change on surface water quality in relation to drinking water production, Environment International 2009, 35(8), 1225-1233.
  • Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell, N.W., Clark, D.B., Dankers, R., Eisner, S., Fekete, B.M., Colon-Gonzalez, F.J., Gosling, S.N., Kim, H., Liu, X., Masaki, Y., Portmann, F.T., Satoh, Y., Stacke, T., Tang, Q., Wada, Y., Wisser, D., Albrecht, T., Frieler, K., Piontek, F., Warszawski, L., Kabat, P. Multimodel assessment of water scarcity under climate change. Proceedings of the National Academy of Sciences 2014, 111(9), 3245-3250.
  • Kellogg W.W. Effects of human activities on global climate: A summary, with consideration of the implications of a possibly warmer Earth. Geneva, Switzerland: Secretariat of the World Meteorological Organization, 1977.
  • Jakob, M., Hilaire, J., Climate science: Unburnable fossil-fuel reserves. Nature 2015, 517(7533), 150-152.
  • Kriegler, E., Mouratiadou, I., Luderer, G., Bauer, N., Brecha, R.J., Calvin, K., de Cian, E., Edmonds, J., Jiang, K., Tavoni, M., Edenhofer, O., Will economic growth and fossil fuel scarcity help or hinder climate stabilization?, Climatic Change 2016, 136(1), 7-22.
  • van der Ploeg, F. Fossil fuel producers under threat, Oxford Review of Economic Policy 2016, 32(2), 206-222.
  • Dresselhaus, M.S., Thomas, I.L. Alternative energy technologies, Nature 2001, 414(6861), 332-337.
  • Eremia M., Liu C.C., Edris A.A. Advanced Solutions in Power Systems: HVDC, FACTS, and Artificial Intelligence, USA, Wiley-IEEE Press, 2016.
  • Droege, P. 100% renewable: Energy autonomy in action. London, UK, Routledge, 2012.
  • Rothe, D. Energy for the masses? Exploring the political logics behind the Desertec vision, J. Int. Relations and Development 2016; 19(3): 392-419.
  • Breyer, C., Bogdanov, D., Komoto, K., Ehara, T., Song, J., Enebish, N. North-east Asian super grid: Renewable energy mix and economics. Japanese J. Applied Physics 2015; 54(8S1): 08KJ01.
  • Gellings, C.W. A globe spanning super grid, IEEE Spectrum, 2015; 52(8): 48-54.
  • Chatzivasileiadis, S., Ernst, D., Andersson, G. The Global Grid, Renewable Energy, 2013; 57: 372–383.
  • Saracoglu, B.O. Global Grid Prediction Systems, DOI: 10.13140/RG.2.1.3575.3040, 2016.
  • Makridakis, S., Wheelwright, S. Integrating forecasting and planning. Long Range Planning 1973; 6(3): 53–63.
  • Das, J.K. Statistics for Business Decisions, Bhawani Dutta Lane, Kolkata, W. Bengal: Academic Publishers, 2012.
  • Jain, C.L., Malehorn, J. Practical Guide to Business Forecasting. New York, USA: Grace Publishing Company, 2005.
  • Soliman, S.A., Al-Kandari, A.M. Electrical Load Forecasting: Modeling And Model Construction. USA: Elsevier, 2010.
  • Chaturvedi, D.K., Soft Computing: Techniques and its Applications in Electrical Engineering. Vol. 103. USA: Springer, 2008.
  • Sachdeva, S., Singh, M., Singh, U.P., Arora, A.S. Efficient Load Forecasting Optimized by Fuzzy Programming and OFDM Transmission, Advances in Fuzzy Systems, 2011, Article ID 326763.
  • Aggarwal, S.K., Kumar, M., Saini, L.M., Kumar, A. Short-Term Load Forecasting in Deregulated Electricity Markets using Fuzzy Approach. J. Engineering and Technology, 2015; 1(1): 24–30.
  • Moreno-Chaparro, C., Salcedo-Lagos, J., Rivas, E., Orjuela Canon, A. A Method for the Monthly Electricity Demand Forecasting in Colombia Based On Wavelet Analysis and A Nonlinear Autoregressive Model. Ingeniería, 2011; 16(2): 94–106.
  • Arfoa, A.A. Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System, Energy and Power Engineering, 2015, 7: 242–253.
  • Arif, M., Nadeem, T., Ali, H., Ali, S.W., Khalid, U., Baseer, M., Short Term Load Forecasting Solution Methodologies: Literature Review, 2013 Survey Paper. IOSR J. Electrical and Electronics Engineering, 2014; 9(3): 44–47.
  • Al-Zahra, K.A., Moosa, K., Jasim, B.H. A Comparative Study of Forecasting the Electrical Demand in Basra City Using Box-Jenkins and Modern Intelligent Techniques. Iraq J. Electrical and Electronic Engineering, 2015; 11(1): 97–110.
  • Demir, A. Elaboration of Electricity Energy for Production-Consumption Relation of Northern-Iraq for the Future Expectations. Int. J. Academic Research in Economics and Management Sciences, 2014; 3(5): 101–106.
  • Hong, W.C. Electric load forecasting by support vector model, Applied Mathematical Modeling, 2009; 33(5): 2444–2454.
  • Yan, Y., Yang, A. Fuzzy Load Forecasting of Electric Power System, J. Computers, 2012, 7(8): 1903–1910.
  • Saracoglu, B.O. Comparative Study on Experimental Type 1 & Interval & General Type 2 Mamdani FIS for G2P3S. J. General Engineering, 2017; 17(2): 27-42.
  • Saracoglu, B.O. Comparative Study on Experimental 2 to 9 Triangular Fuzzy Membership Function Partitioned Type 1 Mamdani's FIS For G2EDPS, J General Engineering 2017; 17(2): 1-18.
  • Saracoglu, B.O. G2EDPS's First Module & Its First Extension Modules, Industrial Engineering, 2017; 1(1): 1-16.
  • Saracoglu, B.O. Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection, Science J. Circuits, Systems and Signal Processing, 2017; 6(2): 25-35.
  • Shleeg, A.A, Ellabib. I.M. Comparison of Mamdani and Sugeno Fuzzy Interference Systems for the Breast Cancer Risk. World Academy of Science, Engineering and Technology, Int. J. Computer, Information, Systems and Control Engineering, 2013; 7(10): 695–699.
  • Kaur, A. Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System, Int. J. Soft Computing and Engineering, 2012; 2(2): 323–325.
  • Salman, M.A., Seno, N.I. A Comparison of Mamdani and Sugeno Inference Systems for a Satellite Image Classification, Anbar J. Engineering Sciences 2002; 0(0): 296–306.
  • Wang, L.X. A Course in Fuzzy Systems and Control. USA: Prentice-Hall International, 1997.
  • Nguyen, H.T., Prasad, N.R., Walker, C.L., Walker, E.A. A First Course in Fuzzy and Neural Control, USA: CRC press, 2002.
  • Wierman, M.J. An Introduction to the Mathematics of Uncertainty. USA: Creighton University. 2010.
  • Gegov, A. Complexity Management in Fuzzy Systems, 711, New York, USA: Springer. 2007.
  • Faraway J.J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. USA: CRC press, 2016.
  • Faraway J.J. Linear Models with R. USA: CRC press, 2014.
  • Sawitzki G. Computational Statistics: An Introduction to R. USA: CRC press, 2009.
  • Kabacoff, R. R in Action: Data analysis and graphics with R. Shelter Island, USA: Manning Publications. 2011.
  • Miller, G.A. The magical number seven, plus or minus two: some limits on our capacity for processing information. The Psychological Review 1956, 63: 81–97.
  • Shiffrin, R. M., Nosofsky, R. M. Seven plus or minus two: a commentary on capacity limitations. Psychological Review 1994; 101(2): 357–361.
Konular Elektrik Elektronik Mühendisliği
Dergi Bölümü Araştırma Makaleleri
Yazarlar

Orcid: orcid.org/0000-0002-2171-2299
Yazar: Burak Omer Saracoglu
E-posta: burakomersaracoglu@hotmail.com
Ülke: Turkey


Bibtex @araştırma makalesi { jes338575, journal = {Journal of Energy Systems}, issn = {}, address = {Erol KURT}, year = {2017}, volume = {1}, pages = {75 - 101}, doi = {}, title = {An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S}, language = {en}, key = {cite}, author = {Saracoglu, Burak Omer} }
APA Saracoglu, B . (2017). An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S. Journal of Energy Systems, 1 (2), 75-101. Retrieved from http://dergipark.gov.tr/jes/issue/31533/338575
MLA Saracoglu, B . "An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S". Journal of Energy Systems 1 (2017): 75-101 <http://dergipark.gov.tr/jes/issue/31533/338575>
Chicago Saracoglu, B . "An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S". Journal of Energy Systems 1 (2017): 75-101
RIS TY - JOUR T1 - An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S AU - Burak Omer Saracoglu Y1 - 2017 PY - 2017 N1 - DO - T2 - Journal of Energy Systems JF - Journal JO - JOR SP - 75 EP - 101 VL - 1 IS - 2 SN - -2602-2052 M3 - UR - Y2 - 2017 ER -
EndNote %0 Journal of Energy Systems An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S %A Burak Omer Saracoglu %T An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S %D 2017 %J Journal of Energy Systems %P -2602-2052 %V 1 %N 2 %R %U