1. A method for determining at least one combined forecast value of non-conventional energy resources by combining one or more forecast values received from one or more predictive forecast models, the method comprising: selecting a historical dataset comprising a first set of forecast values and a first set of actual values received from the one or more predictive forecast models and/or one or more measurements; generating one or more variants of machine learning models to model a performance of the one or more predictive forecast models by training the one or more variants of the machine learning models on the historical dataset; receiving a current dataset comprising a second set of forecast values and a second set of actual values from the one or more predictive forecast models; correlating the current dataset with the historical dataset to obtain a filtered historical dataset; evaluating the one or more variants of the machine learning models trained on the filtered historical dataset; and deriving a statistical model to determine at least one combined forecast value by combining outputs of the one or more variants of the machine learning models trained based on the evaluating of the one or more variants of the machine learning models on the filtered historical dataset, wherein the selecting, the generating, the receiving, the correlating, the evaluating and the deriving are performed by a processor using computer-readable instructions stored in the memory.