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Sustainability 2017, 9(5), 795; doi:10.3390/su9050795

Wind Speed for Load Forecasting Models

1
and
2,3,*
1
Forecasting R&D, SAS Institute Inc., Cary, NC 27513, USA
2
Systems Engineering and Engineering Management Department, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
3
School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 116023, China
*
Author to whom correspondence should be addressed.
Academic Editor: João P. S. Catalão
Received: 25 April 2017 / Revised: 7 May 2017 / Accepted: 8 May 2017 / Published: 10 May 2017
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
View Full-Text   |   Download PDF [3164 KB, uploaded 10 May 2017]   |  

Abstract

Temperature and its variants, such as polynomials and lags, have been the most frequently-used weather variables in load forecasting models. Some of the well-known secondary driving factors of electricity demand include wind speed and cloud cover. Due to the increasing penetration of distributed energy resources, the net load is more and more affected by these non-temperature weather factors. This paper fills a gap and need in the load forecasting literature by presenting a formal study on the role of wind variables in load forecasting models. We propose a systematic approach to include wind variables in a regression analysis framework. In addition to the Wind Chill Index (WCI), which is a predefined function of wind speed and temperature, we also investigate other combinations of wind speed and temperature variables. The case study is conducted for the eight load zones and the total load of ISO New England. The proposed models with the recommended wind speed variables outperform Tao’s Vanilla Benchmark model and three recency effect models on four forecast horizons, namely, day-ahead, week-ahead, month-ahead, and year-ahead. They also outperform two WCI-based models for most cases. View Full-Text
Keywords: load forecasting; wind chill index; wind speed load forecasting; wind chill index; wind speed
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Xie, J.; Hong, T. Wind Speed for Load Forecasting Models. Sustainability 2017, 9, 795.

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