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The higher penetration of renewable resources in the energy portfolios accentuates the need for accurate forecasting of variable resources (solar, wind, tidal) at several different temporal scales to achieve power grid balance. Solar generation technologies have experienced strong energy market growth in the past few years, with corresponding increase in local grid penetration rates. As is the case with wind, the solar resource at the ground level is highly variable mostly due to cloud cover variability, atmospheric aerosol levels, and indirectly, and to a lesser extent, participating gases in the atmosphere. The inherent variability of solar generation at higher grid penetration levels poses problems associated with the cost of reserves, dispatchable and ancillary generation, and grid reliability in general. As a result, high accuracy forecast systems are required for multiple time horizons that are associated with regulation, dispatching, scheduling, and unit commitment. Here we review the theory behind mathematical methods for optimized solar forecasting and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.

This chapter provides an introduction to and overview of the subsequent chapters, which discuss specific solar-forecasting technologies and time horizons. Solar-forecasting methods are classified by technique, time horizon, and application. Advantages and disadvantages of deterministic and stochastic forecasting approaches are laid out and discussed in the context of solar forecasting based on numerical weather prediction, satellite data, and ground measurements. Metrics to evaluate solar-forecasting techniques are then presented and a time horizon–invariant metric is introduced that allows comparing forecast errors across time horizons, geographical regions, and time steps. Finally, the metric is demonstrated with hour-ahead forecasts based on stochastic-learning and satellite cloud-motion vector techniques.

In this chapter, we discuss nonlinear regression and stochastic-learning methods for solar forecasting. A detailed comparison of nonstationary regression methods and different stochastic-learning methods based on kNN, ANN, and GA is presented. A hybrid GA/ANN method emerges as the most robust stochastic candidate to be used as the basis for development of high-fidelity forecast engines. We illustrate different applications of stochastic-learning by considering univariate and multivariate inputs, and we highlight some of the robust qualities of stochastic-learning for a wide range of time horizons.