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A Hybrid Model for Short-Term Solar Irradiance Forecasting using Minimal Data.pdf (1.27 MB)

A Hybrid Online Model for Short-Term Solar Irradiance Forecasting using Minimal Data

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conference contribution
posted on 2024-06-27, 03:18 authored by Thinesh Dharan RahuThinesh Dharan Rahu, Yunyi ZhaoYunyi Zhao, Ryan Kun Keat Peng, Sivaneasan Bala KrishnanSivaneasan Bala Krishnan, Kuan Tak TanKuan Tak Tan, King Jet TsengKing Jet Tseng, Anurag Sharma

Solar photovoltaic (PV) systems have emerged as a prominent renewable energy source (RES) especially in Asia. However, the intermittent and unpredictable nature of solar irradiance poses a significant challenge. Hence, accurate forecasting of solar irradiance is essential to ensure reliable and efficient operation solar PV systems. In this paper, a hybrid model is developed for short-term solar irradiance forecasting. Offline and online data are obtained from a local remote solar PV condition monitoring (CM) system, where real-time environmental details as are used as inputs to achieve results on the proposed online prediction model. Different algorithms were used to perform univariate single step irradiance forecasting based on only historical irradiance data, and subsequently multivariate inputs and time features for the proposed online prediction model were used. Based on the results, it is clear that online method outperforms the offline prediction results, with about 25% improvement on RMSE performance metrics.

History

Journal/Conference/Book title

2023 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE)

Publication date

2024-01-31

Version

  • Pre-print

Rights statement

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Project ID

  • 8537 (R-MOE-A403-F033) Comprehensive Online Condition Monitoring System for Distribution Network Component

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