A Data-Driven Solar Irradiance Forecasting Model with Minimum Data

Cheng Lyu, Sagnik Basumallik, Sara Eftekharnejad, Chongfang Xu

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

Abstract

An emerging new challenge introduced to solar generation forecasting is the accumulation and effective processing of raw weather data. This paper aims to address this challenge by presenting a hybrid approach to forecasting the solar irradiance, incorporating both clustering and feature extraction techniques. The developed method aims to significantly reduce the amount of data required for forecasting, and at the same time increase the accuracy of the forecast. A clustering and data selection strategy is developed that yields a reduced dataset for prediction. The performance of the forecasting approach is evaluated with real solar irradiance data collected throughout the year. Case studies demonstrate that solar irradiance can be accurately forecasted using only 20% of the full-scale training data, while also improving the forecast error compared to using the entire dataset.

Original languageEnglish (US)
Title of host publication2021 IEEE Texas Power and Energy Conference, TPEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728186122
DOIs
StatePublished - Feb 2 2021
Event2021 IEEE Texas Power and Energy Conference, TPEC 2021 - College Station, United States
Duration: Feb 2 2021Feb 5 2021

Publication series

Name2021 IEEE Texas Power and Energy Conference, TPEC 2021

Conference

Conference2021 IEEE Texas Power and Energy Conference, TPEC 2021
Country/TerritoryUnited States
CityCollege Station
Period2/2/212/5/21

Keywords

  • classification
  • clustering
  • Data analytics
  • feature extraction
  • solar generation forecast

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

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