A Machine Learning Framework for Prediction Interval based Technique for Short-Term Solar Energy Forecast

Dhivya Sampath Kumar, Winnie Teo, Ngiap Koh, Anurag Sharma, Wai Lok Woo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The stochastic nature of photovoltaic (PV) power generation can have significant impacts on the power grid stability and reliability. Hence, it is essential to have an accurate forecasting of the PV power generation. In this paper, a new machine learning (ML) framework is presented for short-term solar energy forecast based on prediction interval (PI) technique. Simulation results conducted show how PI is more reliable and accurate as compared to deterministic methods using evaluation metrics.
Original languageEnglish
Title of host publication2020 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages406-409
Number of pages4
ISBN (Electronic)9781665419178
ISBN (Print)9781665430272
DOIs
Publication statusPublished - 26 Dec 2020
Event6th IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering - Online, Bhubaneswar, India
Duration: 26 Dec 202027 Dec 2020
https://wiecon-ece.org/

Publication series

Name2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE)
PublisherIEEE

Conference

Conference6th IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering
Abbreviated titleIEEE WIECON-ECE 2020
CountryIndia
CityBhubaneswar
Period26/12/2027/12/20
Internet address

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