Combining Weather Stations for Electric Load Forecasting

10 years ago, I started looking into how weather data quality issues affect load forecast accuracy. Later, I found that using data from multiple weather stations can help improve the load forecasts (see this SAS white paper). I also invented a weather station selection methodology to automatically select weather stations for a given load zone. After joining UNC Charlotte, I wrote an IJF paper with two collaborators to introduce that methodology. Nowadays many utilities are using it to select their weather stations. Because that IJF paper is reproducible, I often use it as an entrance exam for prospective students interested in joining BigDEAL.

During the past few years, I have been using that IJF paper as a homework problem in my Energy Analytics class. I have been challenging the students to improve the weather station selection methodology. Although the method is hard to beat, every year some students can turn in something better. Last year, I decided to work with the students in the class to write two papers, one on selecting weather stations, and the other on combining weather stations. Right after I made that decision, Antonio Bracale and Pasquale De Falco invited me to write a paper related to ensemble forecasting for a special issue they were editing. Weather station combination apparently fits the scope very well. Although I believed the research deserves publication with a higher tier journal, I accepted the invitation to make this paper open access, with the hope that those who are using the old methodology can upgrade to this new one with minimal effort.

The peer review process was fairly enjoyable. The paper was submitted on March 18, 2019. The first decision, which was a major revision, was sent back to us on April 1, with comments from three reviewers. Most of the review comments were constructive. None of them were as nonsense as some of the reviewers I encountered at IEEE transactions. We submitted the revision on April 8. The paper was accepted on April 12. The editorial office sent me the edited version for proofread on April 16. I was presently surprised that their copy editor did some wordsmith for us. I submitted the proofread version on April 20. The final version was published on April 21.

Citation

Masoud Sobhani, Allison Campbell, Saurabh Sangamwar, Changlin Li, and Tao Hong, "Combining weather stations for electric load forecasting," Energies, vol. 12, no. 8, pp. 1510, April 2019. (open access)

Combining Weather Stations for Electric Load Forecasting

Masoud Sobhani, Allison Campbell, Saurabh Sangamwar, Changlin Li, and Tao Hong

Abstract

Weather is a key factor affecting electricity demand. Many load forecasting models rely on weather variables. Weather stations provide point measurements of weather conditions in a service area. Since the load is spread geographically, a single weather station may not sufficiently explain the variations of the load over a vast area. Therefore, a proper combination of multiple weather stations plays a vital role in load forecasting. This paper answers the question: given a number of weather stations, how should they be combined for load forecasting? Simple averaging has been a commonly used and effective method in the literature. In this paper, we compared the performance of seven alternative methods with simple averaging as the benchmark using the data of the Global Energy Forecasting Competition 2012. The results demonstrate that some of the methods outperform the benchmark in combining weather stations. In addition, averaging the forecasts from these methods outperforms most individual methods.