Coimbra Solar Forecast Engine Lab

The Solar Power Forecasting Initiative

Lowering the Cost and Increasing the Quality of Solar Power Through Research

Carlos F. M. Coimbra, Ph.D.

Associate Professor
Mechanical and Aerospace Engineering
UNIVERSITY OF CALIFORNIA, SAN DIEGO
Jacobs School of Engineering | EBU II Room 554
9500 Gilman Drive #0411 | La Jolla, CA 92093-0411
Phone: (858) 534-4285
Email: ccoimbra.-.at.-.ucsd.edu

Project Summary

At the Forecast Engine Laboratory we strive to develop the highest-fidelity forecasting engines for variable energy resources, mainly related to solar and wind generation. Our engines are also applied to load forecasts, and can be used in a variety of applications where stochastic learning offers a substantial advantage over deterministic methods. Our research interests cover many different areas, including: Atmospheric and Cloud Physics; Fluid Mechanics, Heat and Mass Transfer; Pattern Recognition; Stochastic Learning Methods; Fractional and Variable Order Methods; Nonlinear Chaos Dynamics; Optimization and Regression Methods (GA/ANN/kNN/ARIMA, etc), and Image Processing.

Our goal is to develop solar and wind forecasting engines that span the whole spectrum of temporal and spatial horizons, from intra-minute to multiple days ahead, and from single point radiometers to multi-acre renewable energy farms all the way to continental regions.
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