Next Article in Journal
On-Line Junction Temperature Monitoring of Switching Devices with Dynamic Compact Thermal Models Extracted with Model Order Reduction
Previous Article in Journal
Using a Reactive Transport Simulator to Simulate CH4 Production from Bear Island Basin in the Barents Sea Utilizing the Depressurization Method†
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Energies 2017, 10(2), 186; doi:10.3390/en10020186

k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data

Department of Electrical Engineering, National Taipei University of Technology, 1, Section 3, Zhong-Xiao (Chung-Hsiao) E. Rd., Da’an Dist., Taipei 106, Taiwan
*
Author to whom correspondence should be addressed.
Received: 24 November 2016 / Accepted: 1 February 2017 / Published: 8 February 2017
View Full-Text   |   Download PDF [4139 KB, uploaded 10 February 2017]   |  

Abstract

This paper proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI). The proposed methodology is based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic (PV) energy. This methodology is a combination of k-nearest neighbor (k-NN) algorithm modelling and artificial neural network (ANN) model. The k-NN-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements k-NN as a preprocessing technique prior to ANN method. The error statistical indicators of k-NN-ANN model the mean absolute bias error (MABE) is 42 W/m2 and the root-mean-square error (RMSE) is 242 W/m2. The models forecasts are then compared to measured data and simulation results indicate that the k-NN-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy. View Full-Text
Keywords: global solar irradiance (GSI); photovoltaic (PV); very short term; forecasting; k-nearest neighbor (k-NN); artificial neural network (ANN) global solar irradiance (GSI); photovoltaic (PV); very short term; forecasting; k-nearest neighbor (k-NN); artificial neural network (ANN)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Chen, C.-R.; Kartini, U.T. k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data. Energies 2017, 10, 186.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top