Load Forecasting Tao Hong, PhD Mohammad Shahidehpour, PhD Disclaimer This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The information and studies discussed in this report are intended to provide general information to policy-makers and stakeholders but are not a specific plan of action and are not intended to be used in any State electric facility approval or planning processes. The work of the Eastern Interconnection States’ Planning Council or the Stakeholder Steering Committee does not bind any State agency or Regulator in any State proceeding. 2 Load Forecasting • Short-term • Long-term – Minutes to weeks – Operations – Weeks to decades – Planning (incl. rates) Integrated Load Forecasting: 3 Three Questions • Why load forecasting now? • How to develop and evaluate load forecasts? • What are the insights and findings? 4 Why NOW? • More COMPLICATED than ever before! 5 Why NOW? • More UNPREDICTABLE than ever before! 6 How to Develop? • • • • • • Use HOURLY data!!! Benchmark model Recency effect Weekend effect Holiday effect Macroeconomic indicators 7 How to Evaluate? • Ex post accuracy – actual economy info – actual weather info • K-S statistic • Probabilistic scoring – Pinball loss function – Winkler score –… 8 How to Evaluate? • Pinball loss function – used in GEFCom2014 www.gefcom.org qa: y: forecast at the ath percentile; observation Example #1: observation is 10MW above 90th percentile forecast L = 0.9 x 10 = 9 Example #2: observation is 10MW above 10th percentile forecast L = 0.1 x 10 = 1 9 Case Studies - Territories 10 Case Studies – Weather Stations 11 Case Study – One Example • ComEd (2011 – 2013 forecasts) 12 Insights and Findings • One size no longer fits all: variables (ISO-New England) 13 Insights and Findings • One size no longer fits all: weather stations 14 Insights and Findings • Recession was real… – NCEMC (2009 – 1013 ex ante probabilistic forecasts) 15 Insights and Findings • Recession was real… – NCEMC (2009 – 1013 ex post point forecasts) 16 Takeaways • It’s time to modernize your load forecasting process – Embrace high granular data and recent advancements in forecasting • One size no longer fits all – Spend your efforts customizing the models for each region • All forecasts are wrong – Be realistic about the accuracy, especially in the long run 17 Acknowledgement • This material is based upon work supported by the Department of Energy, National Energy Technology Laboratory, under Award Number DE-OE0000316. • Key project members – Dr. Zuyi Li from IIT – BigDEAL Students from UNCC: Jingrui Xie, Bidong Liu, Jiali Liu and Lili Zhang • Contributors – Members form EISPC studies and white paper workgroup – Members from IEEE Working Group on Energy Forecasting – Colleagues in the energy forecasting community 18 Key References • Tao Hong (2010). Short term electric load forecasting. PhD Dissertation, North Carolina State University. • Tao Hong (2014). Energy forecasting: past, present and future. Foresight: The International Journal of Applied Forecasting, issue 32, 43-48. • Tao Hong, Pu Wang and Laura White (2015). Weather Station Selection for Electric Load Forecasting. International Journal of Forecasting, 31(2), 286295. • Tao Hong, Jason Wilson and Jingrui Xie (2014). Long Term Probabilistic Load Forecasting and Normalization with Hourly Information. IEEE Transactions on Smart Grid, 5(1), 456-462. 19 Thank You Dr. Tao Hong www.drhongtao.com http://blog.drhongtao.com www.otexts.org/book/elf 20