Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

Hongfei Sun, Dongliang Duan, Hongming Zhang, Seong Choi, Jie Luo, Liuqing Yang

Research output: Contribution to conferencePaper


This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decom- pose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high- level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.
Original languageAmerican English
Number of pages5
StatePublished - 2023

Bibliographical note

See NREL/CP-5D00-85202 for preprint

NREL Publication Number

  • NREL/CP-5D00-88263


  • deep learning
  • long short-term memory
  • medium-term load forecasting
  • time coding


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