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

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.