@inproceedings{5141cc44ec104f82b2df43e2bb4bac82,
title = "A Data-Driven Solar Irradiance Forecasting Model with Minimum Data",
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.",
keywords = "Data analytics, classification, clustering, feature extraction, solar generation forecast",
author = "Cheng Lyu and Sagnik Basumallik and Sara Eftekharnejad and Chongfang Xu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Texas Power and Energy Conference, TPEC 2021 ; Conference date: 02-02-2021 Through 05-02-2021",
year = "2021",
month = feb,
day = "2",
doi = "10.1109/TPEC51183.2021.9384949",
language = "English (US)",
series = "2021 IEEE Texas Power and Energy Conference, TPEC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE Texas Power and Energy Conference, TPEC 2021",
}