Day‐ahead residential electricity demand response model based on deep neural networks for peak demand reduction in the jordanian power sector

Ayas Shaqour, Hooman Farzaneh, Huthaifa Almogdady

研究成果: Contribution to journalArticle査読

抄録

In this paper, a comprehensive demand response model for the residential sector in the Jordanian electricity market is introduced, considering the interaction between the power generators (PGs), grid operators (GOs), and service providers (SPs). An accurate day‐ahead hourly short‐term load forecasting is conducted, using deep neural networks (DNNs) trained on four‐year data collected from the National Electric Power Company (NEPCO) in Jordan. The customer behavior is modeled by developing a precise price elasticity matrix of demand (PEMD) based on recent research on the short‐term price elasticity of Jordan’s residential and the analysis of the different types of electrical appliances and their daily operational hours according to the latest surveys. First, the DNNs are fine‐tuned with a detailed feature analysis to predict the day‐ahead hourly electrical demand and achieved a mean absolute percentage error (MAPE) of 1.365% and 1.411% on the validation and test datasets receptively. Then the predictions are used as input to a detailed model of the Jordanian power grid market, where a day‐ahead peak‐time demand response policy for the residential sector is applied to the three distribution power companies in Jordan. Based on different PEMD analyses for the Jordanian residential sector, the results suggest a reduction potential of 5.4% in peak demand accompanied by a cost reduction of USD 154,505 per day for the Jordanian power sector.

本文言語英語
論文番号6626
ジャーナルApplied Sciences (Switzerland)
11
14
DOI
出版ステータス出版済み - 7 2 2021

All Science Journal Classification (ASJC) codes

  • 材料科学(全般)
  • 器械工学
  • 工学(全般)
  • プロセス化学およびプロセス工学
  • コンピュータ サイエンスの応用
  • 流体および伝熱

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