Solar Irradiation Forecasting Technologies: A Review
DOI:
https://doi.org/10.13052/spee1048-4236.391413Keywords:
Forecasting techniques, hybrid models, neural network, solar forecasting, error metrics, SVM, Markov chain, numerical weather prediction.Abstract
Renewable energy has received a lot of attention in the previous two decades
when it comes to meeting electrical needs in the home, industrial, and
agricultural sectors. Solar forecasting is critical for the efficient operation,
scheduling, and balancing of energy generation by standalone and grid-
connected solar PV systems. A variety of models and methods have been
developed in the literature to forecast solar irradiance. This paper provides an
analysis of the techniques used in the literature to forecast solar irradiance.
The main focus of the study is to investigate the influence of meteorological
variables, time horizons, climatic zone, pre-processing technique, optimiza-
tion & sample size on the complexity and accuracy of the model. Due to their
nonlinear complicated problem solving skills, artificial neural network based
models outperform other models in the literature. Hybridizing the two models
or performing pre-processing on the input data can improve their accuracy
even more. It also addresses the various main constituents that influence a
model’s accuracy. The paper provides key findings based on studied literature
to select the optimal model for a specific site. This paper also discusses
the metrics used to measure the efficiency of forecasted model. It has been
observed that the proper selection of training and testing period also enhance
the accuracy of the model.
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