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Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection
Science Journal of Circuits, Systems and Signal Processing
Volume 6, Issue 2, April 2017, Pages: 18-28
Received: Mar. 12, 2017; Accepted: Mar. 29, 2017; Published: Nov. 28, 2017
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Burak Omer Saracoglu, Independent Scholar, Istanbul, Turkey
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Electricity demand (kilowatt hour: kWh) and peak power load (kilowatt: kW) forecasting is very important for not only expansion planning purposes (long term), but also for dispatching purposes (short term). Hence, from the long term forecasting perspective to the very short term forecasting perspective, the nature of electricity demand and the peak power load forecasting has to be studied and understood very well. At first, the problem has to be understood very well, then the solution of this problem has to be studied and solved. These activities are in the scope of this research, development, demonstration, & deployment (RD3) studies. The author thinks that the natural mechanisms of electricity demand and peak power load forecasting problem can be understood very well by finding, defining, identifying, and describing the factors (parameters, variables) that affect the electricity demand and peak power load. In this study, GATE is only used during corpus development as a backup check. R text mining package (Rtm) and TextSTAT are used as main text mining and analysis tools. 314 terms as candidate variable terms are found by this text analysis. Afterwards, all variables are studied and analyzed by a grey based natural reasoning with simple weighted average approach (WA) (only for long term factors as preliminary in this application) (on way of simple additive weighting method: SAW). Finally, 43 terms (e. g. population, weather, climate, economy, price) for variables are found for infant and mature RD3 studies of 100% renewable energy (RE) worldwide grid (Global Grid). Findings of this study can also be used in other grid types. It is believed that a specific dictionary and encyclopedia in this particular subject should be developed for researchers common sense which will also help building of the Global Grid Prediction Systems (G2PS).
Grey, Forecast, Global Grid, Natural Reasoning, Power, Text Analysis
To cite this article
Burak Omer Saracoglu, Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection, Science Journal of Circuits, Systems and Signal Processing. Vol. 6, No. 2, 2017, pp. 18-28. doi: 10.11648/j.cssp.20170602.13
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Friends of the Supergrid "Roadmap to the Supergrid Technologies Final Report". (2012).
Overbye, T. J., Starr, C., Grant, P. M., Schneider, T. R. "National Energy Supergrid Workshop Report". Crowne Plaza Cabana Palo Alto Hotel, Palo Alto, California. (2002).
DESERTEC Foundation, accessed by 11/06/2015. (2015).
Mano, S., Ovgor, B., Samadov, Z., Pudlik, M., Jülch, V., Sokolov, D., Yoon, J. Y. "Gobitec and Asian Super Grid for renewable energies in Northeast Asia". Spotinov print Ltd. (2014).
Seliger, B., Kim, G. E. "Tackling climate change, increasing energy security, engaging North Korea and moving forward Northeast Asian integration – “Green Growth” in Korea and the Gobitec project". Gobitec Outline Paper 1-03 10112009. (2009).
Chatzivasileiadis, S., Ernst, D., Andersson, G. "The Global Grid", Renewable Energy, Vol: 57, pp.372–383. doi: 10.1016/j.renene.2013.01.032. (2013).
ACM Digital Library,
ASCE Online Research Library,
American Society of Mechanical Engineers,
Cambridge Journals Online,
Directory of Open Access Journals,
Emerald Insight,
Google Scholar,
Hindawi Publishing Corporation,
Inderscience Publishers,
Journal of Industrial Engineering and Management,
Science Direct,
Taylor & Francis Online/Journals,
Wiley-Blackwell/Wiley Online Library,
World Scientific Publishing,
Elakrmi, F., Shikhah, N. A. "Electricity Load Forecasting Science and Practices". (2013) (accessed in 29/08/2015).
Hahn, H., Meyer-Nieberg, S., Pickl, S. "Electric load forecasting methods: Tools for decision making". European Journal of Operational Research, Vol: 199, No: 3, pp.902-907. (2009).
Macmillan Dictionary: corpus accessed on 20/01/2016.
Oxford Dictionary: corpus accessed on 20/01/2016.
Longman Dictionaries: corpus accessed on 20/01/2016.
Merriam-Webster: corpus accessed on 20/01/2016.
[28] corpus on 20/01/2016.
Cambridge Dictionaries Online: corpus accessed on 20/01/2016.
Collins Dictionaries: corpus accessed on 20/01/2016.
Manca, E.: Context and Language eISBN 978-88-8305-092-3 (2012).
YouTube: sprachtheater, TextSTAT - Tutorial accessed on 20/01/2016.
Hüning, M.: TextSTAT - Simple Text Analysis Tool. (2001) accessed on 20/01/2016.
R Core Team: R Language Definition (2015).
RPubs by RStudio: Basic Text Mining in R accessed on 20/01/2016
Williams, G. "Hands-On Data Science with R Text Mining". (2014).
Feinerer, I. "Introduction to the tm Package Text Mining in R". (2015).
Grimmer, J., Stewart, B. M. "Text as data: The promise and pitfalls of automatic content analysis methods for political texts". Political Analysis, mps028, pp: 1-31. (2013).
Loughran, T., McDonald, B. "When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks". The Journal of Finance, Vol.66, Issue: 1, pp.35-65. (2011).
O'Connor, B. "Artificial Intelligence (AI) and Social Science, Be careful with dictionary-based text analysis", accessed on 21/01/2016.
Karttunen, L "The logic of infinitival complement constructions" accessed on 23/01/2016.
Karttunen, L. "From Natural Logic to Natural Reasoning" accessed on 23/01/2016.
Terr, David: Weighted Mean From Math World A Wolfram Web Resource, created by Eric W. Weisstein. accessed on 22/01/2016.
Grossman, J., Grossman, M., Katz, R. "The first systems of weighted differential and integral calculus". Archimedes Foundation Box 240, Rockport Massachusetts. (1980).
Price, G. R. "Extension of covariance selection mathematics". Annals of human genetics, Vol: 35, Issue: 4, pp.485-490. (1972).
Liu, S., Fang, Z., Yang, Y., Forrest, J. "General grey numbers and their operations". Grey Systems: Theory and Application, Vol: 2, Issue: 3, pp: 341–349. (2012).
Liu, S., Forrest, J., Yang, Y. "Advances in grey systems research". Journal of Grey System, Vol: 25, Iss: 2, pp.1-18. (2013).
Liu, S., Lin, Y. "Grey information: theory and practical applications". Springer Science & Business Media. (2006).
Saracoglu, B. O. "Global Grid Prediction Systems", DOI: 10.13140/RG.2.1.3575.3040 accessed on 22/01/2016.
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