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

Probabilistic Models for One-Day Ahead Solar Irradiance Forecasting In Renewable Energy Applications on Oahu

File Description Size Format  
2016-05-ms-andradesilva_r.pdf Version for non-UH users. Copying/Printing is not permitted 4.79 MB Adobe PDF View/Open
2016-05-ms-andradesilva_uh.pdf For UH users only 4.78 MB Adobe PDF View/Open

Item Summary

Title:Probabilistic Models for One-Day Ahead Solar Irradiance Forecasting In Renewable Energy Applications on Oahu
Authors:Andrade Silva, Carlos
Keywords:solar forecasting
probability models
1-Day Ahead Forecast
Date Issued:May 2016
Publisher:[Honolulu] : [University of Hawaii at Manoa], [May 2016]
Abstract:In order to produce energy, the Hawaiian Islands rely heavily on oil and oil products to fuel their power plants, leading to high electricity costs that help make renewable energy economically competitive, such as solar energy. Solar energy production, however, introduces a new dimension of uncertainty to meet energy load with supply due to climate conditions: We are not guaranteed to have sufficient solar irradiance available the next day to generate the necessary amount of energy for households and businesses. Forecasting 1-day ahead solar forecasting would then be helpful to know how much energy from other sources are necessary to be produced, in order to compensate the lack of solar energy for the following day.
To address the solar irradiance forecasting need, in this thesis we investigate probabilistic models for one-day ahead solar irradiance forecasting. Namely, we investigate how the use of past solar irradiance and other weather variables (e.g. relative humidity, pressure, temperature, etc.) using one or more sites can influence the accuracy of 1-day ahead solar forecasts. We also discuss how different parameters and limitations encountered throughout the usage of our probability models for solar forecasting influence the forecasts. To address the limitations, we present an entropy based probability model.
Description:M.S. University of Hawaii at Manoa 2016.
Includes bibliographical references.
Appears in Collections: M.S. - Computer Science

Please email if you need this content in ADA-compliant format.

Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.