Factors Affecting Load Forecast Accuracy

In some of my papers, I tried to present fairly comprehensive case studies that cover various load zones. I often use a primary case study to illustrate the flow or components of a proposed methodology. After that I apply the same methodology to a secondary case study to show that the same methodology works well on other zones. A by product of this publication process is a series of benchmark results on various of load zones. You may have realized that the same methodology or model typically results in different forecast errors on different load zones.

A most relevant example was in my IJF paper on weather station selection, where I applied the same methodology to two datasets, one from NCEMC that includes 3 power supply areas and 44 building blocks, the other from GEFCom2012 that includes 20 load zones and the sum of them. The MAPE values across these zones are quite different, with very high MAPEs (double or triple digits) on the industrial load zones. 

In this post, I will have a deeper dive into the factors affecting load forecast accuracy. Here we are concerning short term (point) load forecasting, because there is much more than accuracy to worry about in long term forecasting. 

That said, I'm going to answer the following questions:

What are the factors affecting short term load forecast accuracy?

Here is my list:

1. Data quality. Garbage in, garbage out. If the input data is bad, the forecasts tend to be bad too. In some rare situations, the bad input data may offset some of the model deficiency. 
2. Goodness of the model. If the model is able to capture most of the salient features, and ignore the noise, the forecast must be good. 
3. Load composition. Errors of residential load forecasts are typically lower than those of industrial load forecasts, assuming that the size of loads are similar. Keep in mind that there are easy-to-forecast industrial loads, and hard-to-forecast residential loads.
4. Size of load. The forecast errors (in MAPE) on big loads are typically smaller than the small loads.
5. Time of day. The forecast errors during sleeping hours are typically smaller than the errors during daytime.
6. Season of year. The forecast errors during summer and winter are typically bigger than the errors during spring and fall.
7. Special days. The forecast errors during special days, such as holidays and large local even days, are typically higher than the errors during regular days. 
8. Weather condition. The forecast errors in the areas with stable weather conditions are typically smaller than the place with fast-changing weather conditions.
9. Weather forecasts. A good weather forecast often leads to a good load forecast. 
10. Locations of weather station(s). When the weather stations can properly represent the weather of the territory, the forecasts are typically good. 
11. Size of territory. With the same load level, a large territory typically has bigger errors than a small territory.
12. Hierarchical load. When the load can be further split down the hierarchy, the forecast at top level can be improved. 
13. Error calculation. The errors (in MAPE) of hourly loads are typically higher than those of daily energy, which is higher than those of monthly and annual energy. 
14. Demand side management. Demand response and energy efficiency programs often lead to large errors in load forecasts.
15. Distributed resources. Increase penetration of behind-the-meter solar typically increase the load forecast errors. 
16. Emerging technologies. EV loads add more uncertainties to the conventional loads and tend to increase the load forecast errors. 

Apparently the answer is not trivial. In most if not all of the above bullet points, the answer is not definitive. This is why we need many benchmarking studies to better understand the forecast errors. It doesn't make sense to criticize the MAPE values prior to working on the data. Ironically, this is what many vendors do as part of the sales bluff.

Back to Error Analysis in Load Forecasting.