Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19284
Title: Training and testing of a single-layer LSTM network for near-future solar forecasting
Authors: Halpern-Wight, Naylani 
Konstantinou, Maria 
Charalambides, Alexandros G. 
Reinders, Angèle 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Artificial neural networks;LSTM net;Machine learning;Solar forecasting
Issue Date: Sep-2020
Source: Applied Sciences, 2020, vol. 10, no. 17, articl. no. 5873
Volume: 10
Issue: 17
Journal: Applied Sciences 
Abstract: Increasing integration of renewable energy sources, like solar photovoltaic (PV), necessitates the development of power forecasting tools to predict power fluctuations caused by weather. With trustworthy and accurate solar power forecasting models, grid operators could easily determine when other dispatchable sources of backup power may be needed to account for fluctuations in PV power plants. Additionally, PV customers and designers would feel secure knowing how much energy to expect from their PV systems on an hourly, daily, monthly, or yearly basis. The PROGNOSIS project, based at the Cyprus University of Technology, is developing a tool for intra-hour solar irradiance forecasting. This article presents the design, training, and testing of a single-layer long-short-term-memory (LSTM) artificial neural network for intra-hour power forecasting of a single PV system in Cyprus. Four years of PV data were used for training and testing the model (80% for training and 20% for testing). With a normalized root mean squared error (nRMSE) of 10.7%, the single-layer network performed similarly to a more complex 5-layer LSTM network trained and tested using the same data. Overall, these results suggest that simple LSTM networks can be just as effective as more complicated ones.
URI: https://hdl.handle.net/20.500.14279/19284
ISSN: 20763417
DOI: 10.3390/app10175873
Rights: © by the authors.
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : Cyprus University of Technology 
Eindhoven University of Technology 
University of Twente 
Appears in Collections:Άρθρα/Articles

Files in This Item:
File Description SizeFormat
applsci-10-05873.pdfFulltext430.35 kBAdobe PDFView/Open
CORE Recommender
Show full item record

SCOPUSTM   
Citations

19
checked on Nov 6, 2023

WEB OF SCIENCETM
Citations

16
Last Week
0
Last month
1
checked on Oct 29, 2023

Page view(s) 50

304
Last Week
3
Last month
20
checked on Jun 15, 2024

Download(s)

73
checked on Jun 15, 2024

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons