The International Institute of Forecasters posted
my profile this week.
How did you become a forecaster?
My path to forecasting was more like a maze than a straight line.
In 2005, I joined North Carolina State University’s Electrical Engineering doctoral program. Halfway through my PhD study, in January 2008, I started working in a consulting firm as an electrical engineer, providing services to the energy and utility industries. My first project was on long term spatial load forecasting – forecasting the 30-year ahead annual peak demand for each 50-acre small area of a US utility. In today’s terminology, it is a hierarchical forecasting problem. Knowing almost nothing about forecasting at that time, I formulated the problem as an optimization problem: minimizing the errors in the historical fit and the discrepancies between the sum of lower level forecasts and the upper level forecast, subject to some constraints on saturation load level and load growth rate, etc. I wrote thousands of lines of code in VBA to solve it. I also developed a user interface in MS Excel for power system planners to override the parameters estimated by the computer and see the results on a map. Finally, the solution was very well received by the customer and then sold to many other customers. I also packaged the work into a thesis when I got my master’s degree in operations research and industrial engineering.
At the end of 2008, I was tasked with forecasting hourly electricity demand for another US utility. It was a competition – if my forecast won, the customer would give us a big contract. While the spatial load forecasting project did not require a rigorous evaluation based on the forecast accuracy, this one did. I knew I couldn’t win without getting some statistical forecasting skills. With the help from my wife, a forecaster working at SAS, we developed a linear model that eventually led to a big consulting contract to develop a short term load forecasting solution. In 2009, I joined the Operations Research PhD program of NC State while developing and delivering that short term load forecasting solution at work. I took some time series forecasting courses from David Dickey, who later joined my doctoral committee. In 2010, I completed the dissertation “Short Term Electric Load Forecasting” and received my PhD in operations research and electrical engineering. That’s when I first considered myself a forecaster.
What did you do after getting your PhD?
I continued working in that consulting firm for another few months. In 2011, I got an offer from SAS to work on some forecasting projects for large retailers. The problem was very challenging and interesting to me – how to forecast millions of products on weekly basis? At that time, smart meters were just being deployed in the US. The data would not be ready for analysis for a year or two. I thought it would be nice to take some time off from the utility industry and learn from other industries that had been dealing with hierarchical time series data for decades. I took the offer and became an analytical consultant at SAS for their retail business unit. In January 2012, the General Manager of SAS’ newly formed utilities business unit recruited me to build the energy forecasting vertical. Then I lead a team to commercialize my doctoral research into the SAS Energy Forecasting solution. After the solution was successfully launched, I headed to the next challenge – the workforce crisis in the energy industry. In August 2013,
I came back to academia to become a professor, with the mission of educating the next generation of analysts.
What areas of forecasting interest you?
I’m most interested in energy forecasting, more specifically electricity demand forecasting, an area I’ve been working on since the beginning of my forecasting career. Electricity demand typically comes in with high resolution, long history, strong correlation with weather, and sometimes a hierarchy. We can use the load forecasting problem to demonstrate many forecasting techniques and methodologies. Moreover, the problem is so important because it’s tied to the life quality of billions of people on this planet. In addition to energy forecasting, I also have experience and strong interest in retail forecasting and sports forecasting. Recently, I started working on forecasting problems in the healthcare industry, another fascinating field.
Are you working with companies to improve their forecasting practices?
Yes. I maintain active consulting practices through
Hong Analytics. Every year I teach 5 to 10 training courses internationally, and work on a few consulting projects to tackle some problems that are challenging in nature. These consulting projects and interactions with clients have inspired many novel research ideas. We turn these ideas into scholarly papers and teaching materials. Many other companies use our papers to improve their forecasts and forecasting practices.
What’s your proudest accomplishment in forecasting?
I have several accomplishments to be proud of, such as commercializing both my master thesis and doctoral dissertation research into software solutions, founding the
IEEE Working Group on Energy Forecasting, and authoring a blog on
energy forecasting, Nevertheless, my favorite one is the
Global Energy Forecasting Competition. It was a team effort. Thanks to a group of enthusiastic scholars and the sponsorships from IEEE Power and Energy Society and the IIF, we have organized two competitions so far: GEFCom2012 and GEFCom2014. Both competitions attracted hundreds of participants worldwide. In addition to highlighting the winning methodologies, these competitions have made data publicly available, to encourage and enable reproducible research in the energy forecasting community. We are currently planning for the next competition. Stay tuned :)
What do you do in your free time?
Other than the family time and work time, I love blogging the most. I started my blog Energy Forecasting in 2013 after seeing Rob Hyndman’s blog
Hyndsight. In 2015, the blog attracted 12,119 users from 2,146 cities across 134 countries. In my normal life as a professor living in the peer review system, I had to constantly fight with anonymous reviewers. Blogging is also an escape for me – nobody can reject my post other than myself!
