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Power Demand and Probability Density Forecasting Based on Deep Learning

In: Smart Energy Management

Author

Listed:
  • Kaile Zhou

    (Hefei University of Technology)

  • Lulu Wen

    (Hefei University of Technology)

Abstract

Highly accurate power demand forecasting is important for power system planning and operational decision making. In this chapter, a deep learning (DL) model consisting of multiple hidden layers is used for short-term power demand forecasting. The results are compared with those of some widely used machine learning models, including random forest (RF) and gradient boosting machines. Then, feature engineering is employed to find the most influential factors. Finally, a probability density forecasting method based on quantile regression and DL is established. Three case studies are presented using daily electricity consumption data of three cities of China. The results show that the DL-based approach has higher power demand forecasting accuracy than RF and gradient boosting models. Also, the presented method can obtain high-quality prediction intervals via probability density forecasting.

Suggested Citation

  • Kaile Zhou & Lulu Wen, 2022. "Power Demand and Probability Density Forecasting Based on Deep Learning," Springer Books, in: Smart Energy Management, chapter 0, pages 101-134, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-9360-1_5
    DOI: 10.1007/978-981-16-9360-1_5
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