LONG-TERM PHOTOVOLTAIC GENERATION FORECASTING USING MACHINE LEARNING TECHNIQUES
Keywords:
Photovoltaic Systems, Long-term, Estimate, Solar PotentialAbstract
DOI: https://doi.org/10.46296/ig.v7i14.0224
The following subsequent research articulates the formulation of a predictive solar resource model employing long-term machine learning methodologies where it is built in sequential phases, taking advantage of concepts relevant to historical data analysis and basing its architecture on machine learning methodologies. An exposition of the wide range of machine learning techniques used in global primary resource prediction is presented and criteria are established for the selection of techniques based on their relevance to the different phases of the model, a comprehensive synthesis on the use of primary resource estimation and prediction as integral components of long-term strategic planning of photovoltaic systems is also meticulously prepared; the model is formulated and simulation scenarios are articulated that are relevant to the Ecuadorian climate, which serve as a tool for estimating solar potential that can be used to discern and emphasize the importance of data as fundamental elements in the decision-making processes involved in planning energy sources characterized by the variability of primary resources.
Keywords: Photovoltaic Systems, Long-term, Estimate, Solar Potential.
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