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Monthly electricity consumption forecasting: a step-reduction strategy and autoencoder neural network

Li, Z ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1109/MIAS.2020.3024479
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2021
  4. Abstract:
  5. Accurate monthly electricity consumption forecasting (ECF) can help retailers enhance the profitability in deregulated electricity markets. Most current methods use monthly load data to perform monthly ECF, which usually produces large errors due to insufficient training samples. A few methods try to use fine-grained smart-meter data (e.g., hourly data) to increase training samples. However, such methods still exhibit low accuracy due to the increase in forecasting steps. © 1975-2012 IEEE
  6. Keywords:
  7. Deregulation ; Forecasting ; Neural networks ; Sampling ; Smart meters ; Auto encoders ; Deregulated electricity market ; Electricity consumption forecasting ; Fine grained ; Monthly load ; Reduction strategy ; Training sample ; Electric power utilization
  8. Source: IEEE Industry Applications Magazine ; Volume 27, Issue 2 , 2021 , Pages 90-102 ; 10772618 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9300136