Wind Speed for Load Forecasting Models

One way to categorize the load forecasting papers is based on the variables used in those forecasting models. Because many people who wrote load forecasting papers only had access to the load data with time stamps, they had to propose the models based on the load series only. The representative techniques include exponential smoothing and the ARIMA family. Sometimes people also include the calendar information to come up with some regression models with classification variables. Although these are good and powerful techniques, their real-world applications in load forecasting are very limited. I have criticized those "load-only" models in some of my papers, such as the IJF2016 paper on recency effect:

Both seasonal naïve models perform very poorly compared with the other four models. Seasonal naïve models are used commonly for benchmarking purposes in other industries, such as the retail and manufacturing industries. In load forecasting, the two applications in which seasonal naïve models are most useful are: (1) benchmarking the forecast accuracy for very unpredictable loads, such as household level loads; and (2) comparisons with univariate models. In most other applications, however, the seasonal naïve models and other similar naïve models are not very meaningful, due to the lack of accuracy. 

Weather is must-have in most of the real-world load forecasting models. The most frequently used weather variable in the load forecasting literature is temperature. Some system operators, such as ISO New England, publish temperature data along with the load information. The recent load forecasting competitions, such as GEFCom2012 and GEFCom2014, have also released several years of hourly load and temperature data for benchmarking purpose.

Although non-temperature weather variables have some presence in the load forecasting literature, they are rarely studied in the context of variable selection. Recently we published a TSG paper Relative Humidity for Load Forecasting Models, discussing how to use humidity information to improve load forecasting accuracy. As a sister of that humidity paper, this paper discusses how to include wind speed information in load forecasting models.

Another comment I want to make is on the open access publication. I personally had no interest in publishing my paper with those open access publishers. This is my first try, which turns out to be a good surprise. The reviews were returned to me rather quickly, within 10 days. There were no non-sense comments, so I didn't need to deal with the personal attacks as I normally had to do. Before the final publication, the copy editor helped clean up some typos we had in the submission. From our first submission to the final pagerized version, the whole process took two weeks!

Anyway, hope that you enjoy reading this open access paper!

Citation

Jingrui Xie and Tao Hong, "Wind speed for load forecasting models", Sustainability, vol 9, no 5, pp 795, May, 2017 (open access).

Wind Speed for Load Forecasting Models

Jingrui Xie and Tao Hong

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.