Hugo T. C. Pedro

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We evaluate and compare several forecasting techniques using no exogenous inputs for predicting the solar power output of a 1 MWp, single-axis tracking, photovoltaic power plant operating in Merced, California. The production data used in this work corresponds to hourly averaged power collected from November 2009 to August 2011. Data prior to January 2011(More)
This study proposes an automatic smart adaptive cloud identification (SACI) system for sky imagery and solar irradiance forecast. The system is deployed using off-the-shelf fish-eye cameras that offer substantial advantages in terms of cost when compared to industry-standard sky imagers. SACI uses a smart image categorization (SIC) algorithm that combines(More)
We propose novel smart forecasting models for Direct Normal Irradiance (DNI) that combine sky image processing with Artificial Neural Network (ANN) optimization schemes. The forecasting models, which were developed for over 6 months of intra-minute imaging and irradiance measurements, are used to predict 1 min average DNI for specific time horizons of 5 and(More)
This work proposes a novel forecast methodology for intra-hour solar irradiance based on optimized pattern recognition from local telemetry and sky imaging. The model, based on the k-nearest-neighbors (kNN) algorithm, predicts the global (GHI) and direct (DNI) components of irradiance for horizons ranging from 5 min up to 30 min, and the corresponding(More)
We propose a novel methodology to select candidate locations for solar power plants that take into account solar variability and geographical smoothing effects. This methodology includes the development of maps created by a clustering technique that determines regions of coherent solar quality attributes as defined by a feature which considers both solar(More)
Electric load forecasting is a key element for management and operation of the electric grid. In this study we introduce ensemble re-forecast methods that take an initial forecast and produce a better prediction by extracting information from the structured errors. The models in the ensemble rely upon the real-time information obtained from load(More)
A smart, real-time reforecast method is applied to the intra-hour prediction of power generated by a 48 MWe photovoltaic (PV) plant. This reforecasting method is developed based on artificial neural network (ANN) optimization schemes and is employed to improve the performance of three baseline prediction models: (1) a physical deterministic model based on(More)
We develop a standalone, real-time solar forecasting computational platform to predict one minute averaged solar irradiance ramps ten minutes in advance. This platform integrates cloud tracking techniques using a low-cost fisheye network camera and artificial neural network (ANN) algorithms, where the former is used to introduce exogenous inputs and the(More)
We propose and analyze a spatioetemporal correlation method to improve forecast performance of solar irradiance using gridded satellite-derived global horizontal irradiance (GHI) data. Forecast models are developed for seven locations in California to predict 1-h averaged GHI 1, 2 and 3 h ahead of time. The seven locations were chosen to represent a diverse(More)