Wednesday, December 17, 2014

2014 Greetings from IEEE Working Group on Energy Forecasting

Before I even realize it, the IEEE Working Group on Energy Forecasting is over three years old. This is the third annual greeting message since we started the working group in July 2011. Here I'm listing a few major accomplishments and activities in 2014:

  • The size of IEEE Working Group on Energy Forecasting on LinkedIn has exceeded 1200. This is now the largest energy forecasting group and one of the largest forecasting groups on LinkedIn. 
  • We launched the Global Energy Forecasting Competition 2014, a rolling energy forecasting competition lasting 16 weeks long. The competition attracted hundreds of participants worldwide joining the four tracks, probabilistic load/price/wind/solar forecasting. IEEE PES, the financial sponsor of the competition, made a press release, which was broadcasted by many major media outlets. We have also created a LinkedIn group to facilitate the discussions around the competition. 
  • In 2014, we organized two panel sessions (Integrating Energy Forecasts into Utility Planning and Operations) in IEEE T&D conference and one panel session (Load Forecasting: the State of the Practice) in IEEE PES General meeting, The three sessions combined hosted 16 talks from 13 speakers. 
  • The IEEE tutorial Energy Forecasting in the Smart Grid Era was offered at IEEE PES General Meeting 2014, and was again very well attended and highly rated. 
  • The winning entries of GEFCom2012 have published their work on International Journal of Forecasting (special section on Energy Forecasting) and IEEE Transactions on Smart Grid (special section on Analytics for Energy Forecasting with Applications to Smart Grid). For more information, please read this post

The group has already planned its activities in 2015:

  • We have scheduled a two half-day sessions on IEEE PES General Meeting 2015 to host presentations of the finalists of Global Energy Forecasting Competition 2014.
  • We have been editing a special issue on Probabilistic Energy Forecasting for International Journal of Forecasting. This special issue will collect high quality papers from regular submissions and from winning teams of Global Energy Forecasting Competition 2014. 
  • We will be teaching the IEEE tutorial Energy Forecasting in the Smart Grid Era at IEEE PES General Meeting 2015. 
Look forward to seeing you next year! Stay tuned with working group activities by joining our group on LinkedIn.

Happy holidays and happy forecasting!

Monday, December 15, 2014

Calendar Month and Billing Month

Calendar month is the period of duration from the same date of one month to the same date of the next month, which can be 28, 29 (February during a leap year), 30 or 31. Most countries in the world are using solar calendar.

Prior to the smart grid era, the utilities were sending meter readers to read meters every month. Apparently they were not able to read all the meters at the same time. Since the meter readers work during work days, utilities put the customers into twenty plus groups, one for each workday, called a billing group. On each day, they read the meters from the corresponding billing group. Billing month is just the period between the two adjacent billing days for a billing group. The electricity consumption on a monthly bill was the difference between the two monthly meter readings, which can be quite different from the consumption over a calendar month.

For load forecasting purposes, we would like to map the energy on monthly bills to the energy on calendar months. This often involves a convoluted process, which creates a lot of troubles and conflicts between the accounting and planning departments in a utility. A famous and challenging problem, unbilled energy, was born due to the mismatch between calendar month and billing month. One of the benefits of smart meter deployment is to resolve the unbilled energy problem.

Sunday, December 14, 2014

Multitasking: Three Steps, Three Tools and Three Tips

A very important skill PhD students should pick up in graduate school is "multitasking". I don't believe that human beings can naturally do two brain-consuming tasks at the same time. Reading newspaper during breakfast is not counted as multitasking. In this post, I'm using multitasking to refer to doing multiple tasks over a planning horizon, such as a day, a week, or even a month or longer.

Monday, December 8, 2014

Who's #1? Rating, Ranking and Provisional Leaderboard of GEFCom2014

The 12 evaluation weeks of GEFCom2014 just went by. Many contestants are curious to know about their rankings after such a marathon-type forecasting competition. Last weekend, I created a provisional leaderboard based on the scores I have documented on Inside Leaderboard. In this post, I will share this provisional leaderboard together with the rating and ranking methodology.
Again, this is not the final leaderboard, pending corrections of individual scores (if there were errors) and adjustment of rankings based on the final reports. 

Monday, November 24, 2014

Documentation in Load Forecasting: 4 Reasons and 8 Elements

In load forecasting, especially long term load forecasting, documentation is probably the most important task. The ultimate test of documentation quality is whether the forecasting system has been described in detail so that other people with relevant education background and experience can reproduce the forecasts.

While forecasting is like an adventure, exploring an unknown trail, documentation is like walking the same trail again and again to record what happened in detail. Documentation often requires significant amount of efforts, sometimes more than forecasting itself.

Thursday, November 20, 2014

Load Factor, Coincidence Factor, Diversity factor and Responsibility Factor

Load factor is average load of a system divided by its peak load. The higher the load factor is, the smoother the load profile is, and the more the infrastructure is being utilized. The highest possible load factor is 1, which indicates a flat load profile.

In the old days, load factor is often used for long term peak demand forecasting. The forecasters first develop a energy forecast. They then calculate the average hourly load. Finally by dividing the forecasted average load by a predefined load factor, they can obtain the forecasted peak. However, I would avoid using this method for long term load forecasting in today's world where high resolution data is available for load forecasting.

Tuesday, November 18, 2014

Standard Time, Daylight Saving Time and Local Time

The earth is round like a ball. When it's night in the US, it's morning in China. To do business beyond a local region, people need a common reference to communicate time. Standard time is the synchronization of clocks in different geographical locations within a time zone to a common time standard, usually based on the meridian at the center of the time zone.

Tuesday, November 11, 2014

Prediction Interval and Confidence Interval

This is a pair of terms very difficult to distinguish, because statisticians and economists don't follow the same standard. Since load forecasting falls under the umbrella of forecasting, I'm following the terminology developed by the forecasting community. Special thanks to Rob Hyndman, who answered many questions from me during my preparation of this post. I highly recommend you his two blog posts The difference between prediction intervals and confidence intervals and Prediction intervals too narrow.

In short, there is a simple rule that tells where to use confidence or prediction interval:
A confidence interval is associated with a parameter, while a prediction interval is associated with a prediction. 
Below I'm using three examples to illustrate how to apply these two terms in load forecasting.

Friday, November 7, 2014

Weather, Climate and Temperature

Weather is the condition of the atmosphere, such as temperature, humidity and rainfall, at a particular place over a short period of time, i.e., a few days. For instance, a weather forecast usually goes a few days ahead. Climate refers to the weather pattern of a place over a long period, i.e., a few decades or more. A well-known term is "climate change".

In load forecasting, the most frequently used weather variable is temperature. A temperature station is often called weather station, though a weather station may measure many variables beyond temperature.

Many utilities also include other weather variables as predictors in short term load forecasting models, such as humidity, wind speed and cloud cover. In reality, because it is difficult to forecast these predictors with good accuracy, there is a trade-off between the information gained by adding these additional variables and the noise introduced by their forecast errors. I always go with the principle of parsimony. Unless rigorous tests have been conducted showing the benefits of adding additional variables, I would try to keep the model as lean as possible.

Back to Load Forecasting Terminology

Thursday, November 6, 2014

Resolution (for Hierarchical Load Forecasting) and Resolution (for Probabilistic Load Forecasting)

During the past several decades, utilities have been developing long term load forecasts mostly using monthly data aggregated up to revenue class level or higher. Deployment of smart grid technologies allows utilities to collect data with hourly or sub-hourly interval at household level. Using these "high resolution" data, we can develop load forecasts at various levels in the system, which is called hierarchical load forecasting. There are two aspects of resolution in hierarchical load forecasting:
  • Spatial resolution
Spatial resolution means how many points are being measured in a piece of land. In my master thesis on spatial load forecasting, I divided the service territory of a medium sized utility into 3460 small areas, about 50 acres each. The data was from transformer load management system. In today's world, a "small area" can be 0.2 acre (the size of a typical single family home) or smaller.  While short term load forecasts have been mostly developed based on hourly or half-hourly data, having load information at low levels can help enhance the forecasting accuracy (See One Size No Longer Fits All: Electric Load Forecasting with a Geographic Hierarchy).
  • Temporal resolution
Temporal resolution means the sampling frequency of the meters. In my 2014 TSG paper, a major contribution was to demonstrate the additional forecasting accuracy gained by using high resolution data.

In probabilistic load forecasting, resolution refers to how the size of prediction interval varies at different time periods. A high-resolution probabilistic forecast can properly quantify the uncertainties at different time periods by providing the prediction interval with variable size. For instance, in the figure below, the prediction interval of summer months is much narrower than that of winter months, which tells that load is much more uncertain in winter than in summer.

Back to Load Forecasting Terminology.