Category: Terms and Definitions

. Clear-Sky Model

The power output of a PV plant is a function of location, time, solar-conversion technology, panel area, panel orientation, and, most important, meteorological and climatological conditions. In principle, the dependence of power output on all of these variables, with the exception of meteorological sky conditions, can be modeled deterministically. In clear-sky conditions, output no longer […]


Once the forecasting models are developed using the approaches described in the previous sections, we can turn to assessing their respective performance for comparison. This forecasting-skill assessment is carried out by a number of qualitative and quantitative tests (see Chapter 8). One of the most frequently used techniques for qualitatively assessing forecast accuracy is a […]


15.3.1. GA/ANN: Scanning the Solution Space In general, the following decisions need to be made when creating an ANN-based forecast model: • ANN architecture: number of layers, number of neurons per layer • Preprocessing scheme: smoothing, spectral decomposition, differencing • Fraction and distribution between training and testing data Additionally, ANNs are well suited to multivariate […]


k-Nearest-Neighbors (kNN) is one of the simplest machine-learning algo­rithms. It is a pattern-recognition method for classifying patterns or features (Duda & Hart, 2000). Classification is based on the similarity of a pattern of current values with respect to training samples in the feature space. For the purpose of time-series forecasting, the kNN model consists of […]

ARIMA Models

Unlike stationary processes that may fluctuate around a constant mean, nonsta­tionary processes (such as the solar resource) are distinct in one or more respects in various scales because of diurnal, seasonal, meteorological, and climatological variations. As a result, in the analysis of nonstationary time series, time plays a fundamental role—as the independent variable in a […]

Persistence Methods

One of the simplest methods for predicting the future behavior of a time series is the so-called persistence model. Persistence implies that future values of the time series are calculated on the assumption that conditions remain unchanged between “current” time t and future time t + Th. For a stationary time series—one whose mean and […]