Date of Award
Master of Science
This Thesis focuses on short-term photovoltaic forecasting (STPVF) for the power generation of a solar PV system using probabilistic forecasts and deterministic forecasts. Uncertainty estimation, in the form of a probabilistic forecast, is emphasized in this Thesis to quantify the uncertainties of the deterministic forecasts. Two hybrid intelligent models are proposed in two separate chapters to perform the STPVF. In Chapter 4, the framework of the deterministic proposed hybrid intelligent model is presented, which is a combination of wavelet transform (WT) that is a data filtering technique and a soft computing model (SCM) that is generalized regression neural network (GRNN). Additionally, this chapter proposes a model that is combined as WT+GRNN and is utilized to conduct the forecast of two random days in each season for 1-hour-ahead to find the power generation. The forecasts are analyzed utilizing accuracy measures equations to determine the model performance and compared with another SCM. In Chapter 5, the framework of the proposed model is presented, which is a combination of WT, a SCM based on radial basis function neural network (RBFNN), and a population-based stochastic particle swarm optimization (PSO). Chapter 5 proposes a model combined as a deterministic approach that is represented as WT+RBFNN+PSO, and then a probabilistic forecast is conducted utilizing bootstrap confidence intervals to quantify uncertainty from the output of WT+RBFNN+PSO. In Chapter 5, the forecasts are conducted by furthering the tests done in Chapter 4. Chapter 5 forecasts the power generation of two random days in each season for 1-hour-ahead, 3-hour-ahead, and 6-hour-ahead. Additionally, different types of days were also forecasted in each season such as a sunny day (SD), cloudy day (CD), and a rainy day (RD). These forecasts were further analyzed using accuracy measures equations, variance and uncertainty estimation. The literature that is provided supports that the proposed hybrid intelligent model, WT+RBFNN+PSO, and the uncertainty estimation method bootstrap confidence intervals are a new application for STPVF power generation, thus establishing this Thesis as innovative work.
Received from ProQuest
Alhakeem, Donna, "Solar PV Power Generation Forecasting Using Hybrid Intelligent Algorithms and Uncertainty Quantification Based on Bootstrap Confidence Intervals" (2014). Open Access Theses & Dissertations. 1191.