Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 20, 2025SustainabilityOpen Access

Photovoltaic Energy Modeling Using Machine Learning Applied to Meteorological Variables

View Full Paper
Ask AI
Bookmark
Share

Authors

BCBruno Neves de CamposDMDaniela MaionchiJGJ. S. Gonçalves

Discussion

Loading...

Member takes

Overview

Analysis reveals Random Forest predicts energy generation accurately in photovoltaic systems, highlighting meteorological variables' impact.

Key Points

  • Random Forest model achieved an R2 value of 0.909 for predicting photovoltaic energy generation, indicating high accuracy.
  • The analysis utilized meteorological variables collected from local INMET stations to enhance energy prediction models.
  • Four machine learning techniques were implemented, with Random Forest outperforming others like SARIMAX and LSTM Neural Network.
  • An emphasis on the practical application of machine learning can optimize design and management in renewable energy systems.

Cite This Study

Campos et al. (2025) studied this question.

synapsesocial.com/papers/68af751a7567bf4f94fee431https://doi.org/10.3390/su17167506
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine learning techniques for solar energy generation prediction in photovoltaic systems2025
  2. 2Forecasting Solar Photovoltaic Power Generation: A Machine Learning Time Series Model Approach2025
  3. 3Forecasting Short-Term Photovoltaic Energy Production to Optimize Self-Consumption in Home Systems Based on Real-World Meteorological Data and Machine Learning2025 · 6 citations
  4. 4Explainable Machine Learning and Predictive Statistics for Sustainable Photovoltaic Power Prediction on Areal Meteorological Variables2025
  5. 5A METHOD FOR FORECASTING THE ENERGY GENERATION OF A SOLAR POWER PLANT2025