This work classifies solar photovoltaic power forecasting methods into three major categories i.e., time-series statistical methods, physical methods, and ensemble methods.
This paper proposes a general local learning framework to effectively alleviate the complexities of classifier design by means of "divide and conquer" principle and ensemble method.
Artificial neural networks, support vector machines, kernel methods, and ensemble algorithms are widely known machine learning techniques in biology, medicine, chemistry, and material science [ 1– 10].
These recent studies have focused on improving the effectiveness and efficiency of GP for production scheduling by developing new representations [89], new surrogate-assisted models [45], local search heuristics [97], and ensemble methods [42, 113].
This approach achieves state of the art performance in terms of predictive accuracy and uncertainty quantification in comparison to other approaches in Bayesian neural networks as well as techniques that include Gaussian processes and ensemble methods even when the training data size is relatively small.
These data have been analyzed extensively by many authors with wrapper, filtering and ensemble methods.
We find that both feature selection and ensemble methods improve the performance.
Our predictors use protein primary structure information only and contain three parts: feature generation, classification and ensemble methods.
As a final note, the alert reader will recognize parallels between the above example and ensemble smoothing methods as presented in [17].
Particular attention is devoted to better estimation of model quality, insight into the influence of measurement errors on model quality, and the use of advanced methods such as stacked generalization and ensemble modeling to further improve model quality.
