Irradiance forecasts based on an irradiance monitoring network, cloud motion, and spatial averaging

Antonio T. Lorenzo, William F. Holmgren, Alexander D Cronin

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

We describe and evaluate forecasts of solar irradiance using real-time measurements from a network of irradiance sensors. A forecast method using cloud motion vectors obtained from a numerical weather model shows significant skill over a standard persistence model for forecast horizons from 1 min to over 2 h, although the skill metric may be misleading. To explain this finding, we define and compare several different persistence methods, including persistence methods informed by an instantaneous spatial average of irradiance sensor output and persistence forecasts informed by a time-average of recent irradiance measurements. We show that spatial- or temporal-averaging reduces the forecast RMS errors primarily because these forecasts are smoother (have smaller variance). We use a Taylor diagram, which shows correlation, RMSE, and variance, to more accurately compare several different types of forecasts. Using this diagram, we show that forecasts using the network of sensors have meaningful skill up to 30 min time horizons after which the skill is primarily due to smoothing.

Original languageEnglish (US)
Pages (from-to)1158-1169
Number of pages12
JournalSolar Energy
Volume122
DOIs
StatePublished - Dec 1 2015

Fingerprint

Monitoring
Sensors
Time measurement

Keywords

  • Sensor network
  • Solar forecasting
  • Solar irradiance

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)

Cite this

Irradiance forecasts based on an irradiance monitoring network, cloud motion, and spatial averaging. / Lorenzo, Antonio T.; Holmgren, William F.; Cronin, Alexander D.

In: Solar Energy, Vol. 122, 01.12.2015, p. 1158-1169.

Research output: Contribution to journalArticle

@article{0277e4f44e854da88e149002e0c77422,
title = "Irradiance forecasts based on an irradiance monitoring network, cloud motion, and spatial averaging",
abstract = "We describe and evaluate forecasts of solar irradiance using real-time measurements from a network of irradiance sensors. A forecast method using cloud motion vectors obtained from a numerical weather model shows significant skill over a standard persistence model for forecast horizons from 1 min to over 2 h, although the skill metric may be misleading. To explain this finding, we define and compare several different persistence methods, including persistence methods informed by an instantaneous spatial average of irradiance sensor output and persistence forecasts informed by a time-average of recent irradiance measurements. We show that spatial- or temporal-averaging reduces the forecast RMS errors primarily because these forecasts are smoother (have smaller variance). We use a Taylor diagram, which shows correlation, RMSE, and variance, to more accurately compare several different types of forecasts. Using this diagram, we show that forecasts using the network of sensors have meaningful skill up to 30 min time horizons after which the skill is primarily due to smoothing.",
keywords = "Sensor network, Solar forecasting, Solar irradiance",
author = "Lorenzo, {Antonio T.} and Holmgren, {William F.} and Cronin, {Alexander D}",
year = "2015",
month = "12",
day = "1",
doi = "10.1016/j.solener.2015.10.038",
language = "English (US)",
volume = "122",
pages = "1158--1169",
journal = "Solar Energy",
issn = "0038-092X",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Irradiance forecasts based on an irradiance monitoring network, cloud motion, and spatial averaging

AU - Lorenzo, Antonio T.

AU - Holmgren, William F.

AU - Cronin, Alexander D

PY - 2015/12/1

Y1 - 2015/12/1

N2 - We describe and evaluate forecasts of solar irradiance using real-time measurements from a network of irradiance sensors. A forecast method using cloud motion vectors obtained from a numerical weather model shows significant skill over a standard persistence model for forecast horizons from 1 min to over 2 h, although the skill metric may be misleading. To explain this finding, we define and compare several different persistence methods, including persistence methods informed by an instantaneous spatial average of irradiance sensor output and persistence forecasts informed by a time-average of recent irradiance measurements. We show that spatial- or temporal-averaging reduces the forecast RMS errors primarily because these forecasts are smoother (have smaller variance). We use a Taylor diagram, which shows correlation, RMSE, and variance, to more accurately compare several different types of forecasts. Using this diagram, we show that forecasts using the network of sensors have meaningful skill up to 30 min time horizons after which the skill is primarily due to smoothing.

AB - We describe and evaluate forecasts of solar irradiance using real-time measurements from a network of irradiance sensors. A forecast method using cloud motion vectors obtained from a numerical weather model shows significant skill over a standard persistence model for forecast horizons from 1 min to over 2 h, although the skill metric may be misleading. To explain this finding, we define and compare several different persistence methods, including persistence methods informed by an instantaneous spatial average of irradiance sensor output and persistence forecasts informed by a time-average of recent irradiance measurements. We show that spatial- or temporal-averaging reduces the forecast RMS errors primarily because these forecasts are smoother (have smaller variance). We use a Taylor diagram, which shows correlation, RMSE, and variance, to more accurately compare several different types of forecasts. Using this diagram, we show that forecasts using the network of sensors have meaningful skill up to 30 min time horizons after which the skill is primarily due to smoothing.

KW - Sensor network

KW - Solar forecasting

KW - Solar irradiance

UR - http://www.scopus.com/inward/record.url?scp=84947277352&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84947277352&partnerID=8YFLogxK

U2 - 10.1016/j.solener.2015.10.038

DO - 10.1016/j.solener.2015.10.038

M3 - Article

AN - SCOPUS:84947277352

VL - 122

SP - 1158

EP - 1169

JO - Solar Energy

JF - Solar Energy

SN - 0038-092X

ER -