Direct and Indirect Prediction of Net Demand in Power Systems Based on Syntactic Forecast Engine

Mehdi Bagheri, Kazybek Suieubek, Oveis Abedinia, Mohammad Salay Naderi, Mehdi Salay Naderi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Combination of green energy resources i.e., wind and solar, into electrical power systems is quickly increased in the world. High volatility of such resources made the operation of power system challengeable. For this purpose, short term forecasting is considered as one of the solutions. This solution can be applied to the net demand directly (load forecast minus renewable generation forecast) or it can be applied to the power system indirectly. In this study we have analyzed the proposed two models to show desirable method based on hybrid forecasting approach. The proposed forecasting model consists of three block cascade neural network based on an intelligent algorithm. All parameters of neural network based forecast engine is optimized by intelligent algorithm to increase the prediction accuracy. The proposed forecasting model is then applied on different test cases in various markets and generated results are compared with the results of various prediction models. These comparisons proof the validity of the improved prediction method.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538651858
DOIs
Publication statusPublished - Oct 16 2018
Event2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018 - Palermo, Italy
Duration: Jun 12 2018Jun 15 2018

Other

Other2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018
CountryItaly
CityPalermo
Period6/12/186/15/18

Keywords

  • Direct and indirect forecasting
  • Hybrid Forecast Engine
  • Net Demand
  • SSO

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Environmental Engineering
  • Hardware and Architecture

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