Can we use Chinese calendar to forecast the load in the U.S.? Since I started my load forecasting practice 10 years ago, this has been a question sitting in my mind. One year ago, we decided to the test this idea. In short, the answer is YES. In the big data era, this approach would fall in the category of leveraging a variety of data sources.
This paper will be collected in the MPCE special section "Forecasting in Modern Power Systems" (Call For Papers). The paper is open access, so you can read the full content and download the PDF file for free. Special thanks to the journal editorial office for the neat copy-editing work. I truly enjoyed the publication process. Unlike most other open access journals that charge the authors a big fee for publishing the papers, this one does not charge a dime. I would highly recommend this journal to those who are interested in publishing open access papers in energy forecasting but do not want to pay for the publication fees.
Jingrui Xie and Tao Hong, "Load forecasting using 24 solar terms," Journal of Modern Power Systems and Clean Energy, in press, available online. (open access)
Load Forecasting Using 24 Solar Terms
Jingrui Xie and Tao Hong
Calendar is an important driving factor of electricity demand. Therefore, many load forecasting models would incorporate calendar information. Frequently used calendar variables include hours of a day, days of a week, months of a year, and so forth. During the past several decades, a widely-used calendar in load forecasting is the Gregorian calendar from the ancient Rome, which dissects a year into 12 months based on the Moon’s orbit around the Earth. The applications of alternative calendars have rarely been reported in the load forecasting literature. This paper aims at discovering better means than Gregorian calendar to categorize days of a year for load forecasting. One alternative is the solar-term calendar, which divides the days of a year into 24 terms based on the Sun’s position in the zodiac. It was originally from the ancient China to guide people for their agriculture activities. This paper proposes a novel method to model the seasonal change for load forecasting by incorporating the 24 solar terms in regression analysis. The case study is conducted for the eight load zones and the system total of ISO New England. Results from both cross-validation and sliding simulation show that the forecast based on the 24 solar terms is more accurate than its counterpart based on the Gregorian calendar.