Estimating Harvestable Solar Energy from Atmospheric Pressure Using Support Vector Regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096787" target="_blank" >RIV/61989100:27240/15:86096787 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/INCoS.2015.58" target="_blank" >http://dx.doi.org/10.1109/INCoS.2015.58</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/INCoS.2015.58" target="_blank" >10.1109/INCoS.2015.58</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimating Harvestable Solar Energy from Atmospheric Pressure Using Support Vector Regression

Popis výsledku v původním jazyce

Energy neutrality is the desired mode of operation of many sensor networks used for environmental monitoring. Intelligent energy harvesting networks, composed of nodes equipped with solar panels and other types of power-scavenging devices, can plan and manage their operations according to short and long-term predictions of ambient energy availability. This paper introduces a novel method for next-day solar energy prediction based on atmospheric pressure and support vector regression. A location-specificsupport vector regression model is in this approach created using a collection of geospatially correlated atmospheric pressure and solar intensity measurements. The trained model is used to estimate next day solar energy availability from a time seriesof recent atmospheric pressure values and their differences. The ability of the proposed system to estimate daily solar energy is compared to a recent evolutionary-fuzzy prediction scheme and traditional analytical estimates.

Název v anglickém jazyce

Estimating Harvestable Solar Energy from Atmospheric Pressure Using Support Vector Regression

Popis výsledku anglicky

Energy neutrality is the desired mode of operation of many sensor networks used for environmental monitoring. Intelligent energy harvesting networks, composed of nodes equipped with solar panels and other types of power-scavenging devices, can plan and manage their operations according to short and long-term predictions of ambient energy availability. This paper introduces a novel method for next-day solar energy prediction based on atmospheric pressure and support vector regression. A location-specificsupport vector regression model is in this approach created using a collection of geospatially correlated atmospheric pressure and solar intensity measurements. The trained model is used to estimate next day solar energy availability from a time seriesof recent atmospheric pressure values and their differences. The ability of the proposed system to estimate daily solar energy is compared to a recent evolutionary-fuzzy prediction scheme and traditional analytical estimates.

Druh

D - Stať ve sborníku

CEP obor

JB - Senzory, čidla, měření a regulace

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Intelligent Networking and Collaborative Systems INCoS-2015 : 7th International Conference : proceedings : September 2-4, 2015, Taipei, Tchaj-wan

ISBN

978-1-4673-7694-5

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

192-199

Název nakladatele

IEEE

Místo vydání

Danvers

Místo konání akce

Taipei

Datum konání akce

2. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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