Day-ahead photovoltaic power forecasting using hybrid K-Means++ and improved deep neural network

Lu, X

Day-ahead photovoltaic power forecasting using hybrid K-Means++ and improved deep neural network

Lu, X

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Measurement: Journal of the International Measurement Confederation, 2023, 220, pp. 113208
Issue Date:: 2023-10-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0263224123007728-main.pdf	Published version	3.11 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lu, X
dc.date.accessioned	2024-04-20T20:50:38Z
dc.date.available	2024-04-20T20:50:38Z
dc.date.issued	2023-10-01
dc.identifier.citation	Measurement: Journal of the International Measurement Confederation, 2023, 220, pp. 113208
dc.identifier.issn	0263-2241
dc.identifier.uri	http://hdl.handle.net/10453/178123
dc.description.abstract	The relationship between environmental factors with the power output of photovoltaic (PV) stations is unclear due to the non-linear characteristics of PV systems, which is challenging for PV power forecasting technology. To cope with these challenges, a hybrid forecasting approach called hybrid K-Means++ and Deep Neural Network with input and output adjusting structures (K-IAOA-DNN) is proposed to accurately predict PV power output. The proposed forecasting approach designs several features for K-Means++ to search for similar samples, reducing the complexity of the following forecasting model. Due to changeable environmental conditions and characteristics of PV systems like degradation, the prediction result of a forecasting model may deviate from the expected one. Therefore, IAOA-DNN model is developed by using a DNN adopting the two proposed structures e.g., the input adjusting structure and the output adjusting structure. The input adjusting structure uses several trainable parameters to determine the importance of each meteorological input for further feature extracting by DNN. The output structure in the prediction model is used for analyzing features extracted from DNN to generate a scalar factor fine-tuning the output of DNN. Additionally, the proposed method provides more accurate forecasting results with average RMSE, NRMSE, and MAE values of 14.43, 0.048, and 9.53 kW, respectively.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Measurement: Journal of the International Measurement Confederation
dc.relation.isbasedon	10.1016/j.measurement.2023.113208
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0913 Mechanical Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Day-ahead photovoltaic power forecasting using hybrid K-Means++ and improved deep neural network
dc.type	Journal Article
utslib.citation.volume	220
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-20T20:50:35Z
pubs.publication-status	Published
pubs.volume	220

Abstract:

The relationship between environmental factors with the power output of photovoltaic (PV) stations is unclear due to the non-linear characteristics of PV systems, which is challenging for PV power forecasting technology. To cope with these challenges, a hybrid forecasting approach called hybrid K-Means++ and Deep Neural Network with input and output adjusting structures (K-IAOA-DNN) is proposed to accurately predict PV power output. The proposed forecasting approach designs several features for K-Means++ to search for similar samples, reducing the complexity of the following forecasting model. Due to changeable environmental conditions and characteristics of PV systems like degradation, the prediction result of a forecasting model may deviate from the expected one. Therefore, IAOA-DNN model is developed by using a DNN adopting the two proposed structures e.g., the input adjusting structure and the output adjusting structure. The input adjusting structure uses several trainable parameters to determine the importance of each meteorological input for further feature extracting by DNN. The output structure in the prediction model is used for analyzing features extracted from DNN to generate a scalar factor fine-tuning the output of DNN. Additionally, the proposed method provides more accurate forecasting results with average RMSE, NRMSE, and MAE values of 14.43, 0.048, and 9.53 kW, respectively.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178123