Impact of network layout and time resolution on spatio-temporal solar forecasting

•Spatially distributed solar data used as input for ARX forecasting.•Different spatial and temporal resolutions explored.•Two different forecasting modes identified.•High forecasting skills for ultra short term horizons depend on network layout.•Forecasting skill for longer horizons independent of n...

Full description

Saved in:
Bibliographic Details
Published in:Solar energy 2018-03, Vol.163, p.329-337
Main Authors: Amaro e Silva, R., C. Brito, M.
Format: Article
Language:English
Subjects:
ARX model
Autoregressive models
Energy resources
Forecasting
Layouts
Parameters
Photovoltaics
Power plants
PV integration
Sensitivity analysis
Sensors
Solar energy
Solar power
Solar radiation
Spatial smoothing
Spatio-temporal forecasting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Spatially distributed solar data used as input for ARX forecasting.•Different spatial and temporal resolutions explored.•Two different forecasting modes identified.•High forecasting skills for ultra short term horizons depend on network layout.•Forecasting skill for longer horizons independent of network layout. Spatio-temporal solar forecasting uses spatially distributed solar radiation or photovoltaic power data to enhance the forecasting at a given site. Two data sets with a wide range of time and spatial resolutions are explored using linear Auto-Regressive models with eXogenous inputs (ARX). Results allow the identification of two different forecasting modes of operation. A short-term mode, where suitable neighbours may significantly improve the forecasting performance, with skill values up to 30–40%, as they provide information on incoming clouds, and a longer-term mode, where the neighbouring sensors’ positioning is less relevant as the positive skill values around 10–20% are associated to a spatial smoothing effect which reduces the occurrence of high forecast errors. For the short-term mode, the correlation between forecast horizons and effective distance to the most contributing neighbours was shown by a normalized weighted average distance (nWAD) parameter. Additionally, this parameter further sustained that the sensor network layout is not relevant for the second mode.
ISSN:0038-092X
1471-1257
DOI:10.1016/j.solener.2018.01.095