Abstract
Power utilities' demand for renewable energy resources is increasing, but the complexities of integrating conventional and renewable resources into the active networked distribution system can be prohibitive. Wind power is the most popular renewable resource worldwide due to its efficiency and cost. Effective planning of wind power integration requires accurate wind speed modeling. However, achieving this accuracy is challenging. New research is needed to determine the most accurate modeling method for wind power. In this paper, probability distribution function (pdf), Markov chain and Auto-Regressive Moving Average (ARMA) are evaluated for accuracy in modeling to forecast short- and long-term wind speed and power, and the ease of integrating wind power into the system.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE Power and Energy Society General Meeting, PESGM 2015 |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9781467380409 |
| DOIs | |
| State | Published - 30 Sep 2015 |
Publication series
| Name | IEEE Power and Energy Society General Meeting |
|---|---|
| Volume | 2015-September |
| ISSN (Print) | 1944-9925 |
| ISSN (Electronic) | 1944-9933 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- ARMA
- Goodness-of-fit
- Markov Chain
- Wind Speed
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Nuclear Energy and Engineering
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
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