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).
| Published in | Science Journal of Circuits, Systems and Signal Processing (Volume 6, Issue 2) |
| DOI | 10.11648/j.cssp.20170602.13 |
| Page(s) | 18-28 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Grey, Forecast, Global Grid, Natural Reasoning, Power, Text Analysis
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APA Style
Burak Omer Saracoglu. (2017). Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection. Science Journal of Circuits, Systems and Signal Processing, 6(2), 18-28. https://doi.org/10.11648/j.cssp.20170602.13
ACS Style
Burak Omer Saracoglu. Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection. Sci. J. Circuits Syst. Signal Process. 2017, 6(2), 18-28. doi: 10.11648/j.cssp.20170602.13
AMA Style
Burak Omer Saracoglu. Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection. Sci J Circuits Syst Signal Process. 2017;6(2):18-28. doi: 10.11648/j.cssp.20170602.13
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Download@article{10.11648/j.cssp.20170602.13,
author = {Burak Omer Saracoglu},
title = {Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection},
journal = {Science Journal of Circuits, Systems and Signal Processing},
volume = {6},
number = {2},
pages = {18-28},
doi = {10.11648/j.cssp.20170602.13},
url = {https://doi.org/10.11648/j.cssp.20170602.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20170602.13},
abstract = {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).},
year = {2017}
}
TY - JOUR T1 - Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection AU - Burak Omer Saracoglu Y1 - 2017/11/28 PY - 2017 N1 - https://doi.org/10.11648/j.cssp.20170602.13 DO - 10.11648/j.cssp.20170602.13 T2 - Science Journal of Circuits, Systems and Signal Processing JF - Science Journal of Circuits, Systems and Signal Processing JO - Science Journal of Circuits, Systems and Signal Processing SP - 18 EP - 28 PB - Science Publishing Group SN - 2326-9073 UR - https://doi.org/10.11648/j.cssp.20170602.13 AB - 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). VL - 6 IS - 2 ER -
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